# Unconstrained Biometric Recognition: Summary of Recent SOCIA Lab. Research

Varsha Balakrishnan
University of Beira Interior, Portugal University of Beira Interior, R.
Marquês D’Avila e Bolama, 6200-001, Covilhã, Portugal, E-mail:
<EMAIL_ADDRESS>
###### Abstract

The development of biometric recognition solutions able to work in visual
surveillance conditions, i.e., in unconstrained data acquisition conditions
and under covert protocols has been motivating growing efforts from the
research community. Among the various laboratories, schools and research
institutes concerned about this problem, the SOCIA: Soft Computing and Image
Analysis Lab., of the University of Beira Interior, Portugal, has been among
the most active in pursuing disruptive solutions for obtaining such extremely
ambitious kind of automata. This report summarises the research works
published by elements of the SOCIA Lab. in the last decade in the scope of
biometric recognition in unconstrained conditions. The idea is that it can be
used as basis for someone wishing to entering in this research topic.

###### Index Terms:

Biometric recognition, iris recognition, visual surveillance, image
segmentation.

##  I Data Acquisition Works

### Visible-wavelength Iris / Periocular Imaging and Recognition in
Surveillance Environments [58]

Visual surveillance cameras have been massively deployed in public urban
environments over the recent years. This raised the interest in developing
automata to infer useful information from such crowded scenes (from abnormal
behavior detection to human identification). In order to cover wide outdoor
areas, one interesting possibility is to combine wide- angle and pan-tilt-zoom
(PTZ) cameras in a master-slave configuration. The use of fish-eye lenses
allows the master camera to maximize the coverage area while the PTZ acts as a
foveal sensor, providing high-resolution images of the interest regions. This
paper addresses the feasibility of using this type of data acquisition
paradigm for imaging iris/periocular data with enough discriminating power to
be used for biometric recognition purposes.

### A Master-slave Calibration Algorithm with Fish-eye Correction [13]

Surveillance systems capable of autonomously monitoring vast areas are an
emerging trend, particularly when wide-angle cameras are combined with pan-
tilt-zoom (PTZ) cameras in a master-slave configuration. The use of fish-eye
lenses allows the master camera to maximize the coverage area while the PTZ
acts as a foveal sensor, providing high-resolution images of regions of
interest. Despite the advantages of this architecture, the mapping between
image coordinates and pan-tilt values is the major bottleneck in such systems,
since it depends on depth information and fish-eye effect correction. In this
paper, authors address these problems by exploiting geometric cues to perform
height estimation. This information is used both for inferring 3D information
from a single static camera deployed on an arbitrary position and for
determining lens parameters to remove fish-eye distortion. When compared with
the previous approaches, this method has the following advantages: (1) fish-
eye distortion is corrected without relying on calibration patterns; (2) 3D
information is inferred from a single static camera disposed on an arbitrary
location of the scene.

Figure 1: Schema of a non-cooperative data acquisition framework.

### Biometric Recognition in Surveillance Environments Using Master-Slave
Architectures [63]

The number of visual surveillance systems deployed worldwide has been growing
astoundingly. As a result, attempts have been made to increase the levels of
automated analysis of such systems, towards the reliable recognition of human
beings in fully covert conditions. Among other possibilities, master-slave
architectures can be used to acquire high resolution data of subjects heads
from large distances, with enough resolution to perform face recognition. This
paper/tutorial provides a comprehensive overview of the major phases behind
the development of a recognition system working in outdoor surveillance
scenarios, describing frameworks and methods to: 1) use coupled wide view and
Pan-Tilt-Zoom (PTZ) imaging devices in surveillance settings, with a wide-view
camera covering the whole scene, while a synchronized PTZ device collects
high-resolution data from the head region; 2) use soft biometric information
(e.g., body metrology and gait) for pruning the set of potential identities
for each query; and 3) faithfully balance ethics/privacy and safety/security
issues in this kind of systems.

### Acquiring High-resolution Face Images in Outdoor Environments: A master-
slave Calibration Algorithm [14]

Facial recognition at-a-distance in surveillance scenarios remains an open
problem, particularly due to the small number of pixels representing the
facial region. The use of pan-tilt-zoom (PTZ) cameras has been advocated to
solve this problem, however, the existing approaches either rely on rough
approximations or additional constraints to estimate the mapping between image
coordinates and pan-tilt parameters. In this paper, authors aim at extending
PTZ-assisted facial recognition to surveillance scenarios by proposing a
master-slave calibration algorithm capable of accurately estimating pan-tilt
parameters without depending on additional constraints. This paper exploits
geometric cues to automatically estimate subjects height and thus determine
their 3D position. Experimental results show that the presented algorithm is
able to acquire high-resolution face im- ages at a distance ranging from 5 to
40 meters with high success rate. Additionally, authors certify the
applicability of the aforementioned algorithm to biometric recognition through
a face recognition test, comprising 20 probe subjects and 13,020 gallery
subjects.

### Dynamic Camera Scheduling for Visual Surveillance in Crowded Scenes using
Markov Random Fields [15]

The use of pan-tilt-zoom (PTZ) cameras for capturing high-resolution data of
human-beings is an emerging trend in surveillance systems. However, this new
paradigm en- tails additional challenges, such as camera scheduling, that can
dramatically affect the performance of the system. This paper presents a
camera scheduling approach capable of determining - in real-time - the
sequence of acquisitions that maximizes the number of different targets
obtained, while minimizing the cumulative transition time. This approach
models the problem as an undirected graphical model (Markov random field,
MRF), which energy minimization can approximate the shortest tour to visit the
maximum number of targets. A comparative analysis with the state-of-the-art
camera scheduling methods evidences that this approach is able to improve the
observation rate while maintaining a competitive tour time.

### A Calibration Algorithm for Multi-camera Visual Surveillance Systems Based
on Single-View Metrology [16]

The growing concerns about persons security and the increasing popularity of
pan-tilt-zoom (PTZ) cameras, have been raising the interest on automated
master-slave surveillance systems. Such systems are typically composed by (1)
a fixed wide-angle camera that covers a large area, detects and tracks moving
objects in the scene; and (2) a PTZ camera, that provides a close-up view of
an object of interest. Previously published approaches attempted to establish
2D correspondences between the video streams of both cameras, which is a ill-
posed formulation due to the absence of depth information. On the other side,
3D-based approaches are more accurate but require more than one fixed camera
to estimate depth information. This paper describes a novel method for easy
and precise calibration of a master-slave surveillance system, composed by a
single fixed wide-angle camera. The method exploits single view metrology to
infer 3D data of the tracked humans and to self-perform the transformation
between camera views. Experimental results in both simulated and realistic
scenes point for the effectiveness of the proposed model in comparison with
the state-of-the-art

##  II Databases

### An Annotated Multi-biometrics Data Feed From Surveillance Scenarios [21]

The accuracy of biometric recognition in unconstrained scenarios has been a
major concern for a large number of researchers. Despite such efforts, no
system can recognize in a fully automated manner human beings in totally wild
conditions, such as in surveillance environments. In this context, several
sets of degraded data have been made available to the research community,
where the reported performance by state-of-the-art algorithms is already
saturated, suggesting that these sets do not reflect faithfully the conditions
in such hard settings. To this end, authors introduce the QUIS-CAMPI data
feed, comprising samples automatically acquired by an outdoor visual
surveillance system, with subjects on-the-move and at-a-distance (up to 50 m).
When compared to similar data sources, the major novelties of QUIS-CAMPI are:
1) biometric samples are acquired in a fully automatic way; 2) it is an open
dataset, i.e., the number of probe images and enrolled subjects grow on a
daily basis; and 3) it contains multi-biometric traits. The ensemble
properties of QUIS-CAMPI ensure that the data span a representative set of
covariate factors of real-world scenarios, making it a valuable tool for
developing and benchmarking biometric recognition algorithms capable of
working in unconstrained scenarios.

### BioHDD: A Dataset for Studying Biometric Identification on Heavily
Degraded Data [76]

This study focuses on biometric recognition in extremely degraded data, and
its main contributions are three-fold: (1) announce the availability of an
annotated dataset that contains high quality mugshots of 101 subjects, and
large sets of probes degraded extremely by 10 different noise factors; (2)
report the results of a mimicked watchlist identification scheme: an online
survey was conducted, where participants were asked to perform positive and
negative identification of probes against the enrolled identities. Along with
their answers, volunteers had to provide the major reasons that sustained
their responses, which enabled the authors to perceive the kind of features
that are most frequently associated with successful/failed human
identification processes. As main conclusions, the authors observed that
humans rely greatly on shape information and holistic features. Otherwise,
colour and texture-based features are almost disregarded by humans; (3)
finally, the authors give evidence that the positive human identification on
such extremely degraded data might be unreliable, whereas negative
identification might constitute an interesting alternative for such cases.

### Iris Biometrics: Synthesis of Degraded Ocular Images [7]

Iris recognition is a popular technique for recognizing humans. However, as is
the case with most biometric traits, it is difficult to collect data that are
suitable for use in experiments due to three factors: 1) the substantial
amount of data that is required; 2) the time that is spent in the acquisition
process; and 3) the security and privacy concerns of potential volunteers.
This paper describes a stochastic method for synthesizing ocular data to
support experiments on iris recognition. Specifically, synthetic data are
intended for use in the most important phases of those experiments:
segmentation and signature encoding/matching. The resulting data have an
important characteristic: they simulate image acquisition under uncontrolled
conditions. authors have experimentally confirmed that the proposed strategy
can mimic the data degradation factors that usually result from such
conditions. Finally, authors announce the availability of an online platform
for generating degraded synthetic ocular data, freely accessible worldwide.

### The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-
The-Move and At-A-Distance [49]

The main purpose of this paper is to announce the availability of the
UBIRIS.v2 database, a multisession iris images database which singularly
contains data captured in the visible wavelength, at-a-distance (between four
and eight meters) and on on-the-move. This database is freely available for
researchers concerned about visible wavelength iris recognition and will be
useful in accessing the feasibility and specifying the constraints of this
type of biometric recognition.

### UBEAR: A Dataset of Ear Images Captured On-the-move in Uncontrolled
Conditions [70]

In order to broad the applicability of biometric systems, the data acquisition
constraints required for reliable recognition are receiving increasing
attention. For some of the traits (e.g., face and iris) significant research
efforts were already made toward the development of systems able to operate in
completely unconstrained conditions. For other traits (e.g., the ear) no
similar efforts are known. The main purpose of this paper is to announce the
availability of a new data set of ear images, which main distinguishing
feature is that its images were acquired from on-the-move subjects, under
varying lighting conditions and without demanding to subjects any particular
care regarding ear occlusions and poses. The data set is freely available to
the research community and should constitute a valuable tool in assessing the
possibility of performing reliable ear biometric recognition in such d
challenging conditions.

### UBIRIS: a noisy iris image database [38]

This paper presents a new iris database that contains images with noise. This
is in contrast with the existing databases, that are noise free. UBIRIS is a
tool for the development of robust iris recognition algorithms for biometric
proposes. authors present a detailed description of the many characteristics
of UBIRIS and a comparison of several image segmentation approaches used in
the current iris segmentation methods where it is evident their small
tolerance to noisy images.

##  III Segmentation Works

Figure 2: Iris segmentation step. receiving one close up image of the ocular
region, the iris boundaries should be discriminated and parameterized.

### Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and
Style Recognition based on Hierarchical MRFs[59]

Markov Random Fields (MRFs) are a popular tool in many computer vision
problems and faithfully model a broad range of local dependencies. However,
rooted in the Hammersley-Clifford theorem, they face serious difficulties in
enforcing the global coherence of the solutions without using too high order
cliques that reduce the computational effectiveness of the inference phase.
Having this problem in mind, authors describe a multi-layered (hierarchical)
architecture for MRFs that is based exclusively in pairwise connections and
typically produces globally coherent solutions, with 1) one layer working at
the local (pixel) level, modelling the interactions between adjacent image
patches; and 2) a complementary layer working at the object (hypothesis) level
pushing toward globally consistent solutions. During optimization, both layers
interact into an equilibrium state, that not only segments the data, but also
classifies it. The proposed MRF architecture is particularly suitable for
problems that deal with biological data (e.g., biometrics), where the
reasonability of the solutions can be objectively measured.

### Iris Recognition: On the Segmentation of Degraded Images Acquired in the
Visible Wavelength [31]

The imaging conditions engender acquired noisy artefacts that lead to severely
degraded images, making iris segmentation a major issue. Having observed that
existing iris segmentation methods tend to fail in these challenging
conditions, authors present a segmentation method that can handle degraded
images acquired in less constrained conditions. In this paper authors offer
the following contributions: 1) to consider the sclera the most easily
distinguishable part of the eye in degraded images, 2) to propose a new type
of feature that measures the proportion of sclera in each direction and is
fundamental in segmenting the iris, and 3) to run the entire procedure in
deterministically linear time in respect to the size of the image, making the
procedure suitable for real-time applications.

### Iris Segmentation Methodology for Non-Cooperative Recognition [39]

An overview of the iris image segmentation methodologies for biometric
purposes is presented. The main focus is on the analysis of the ability of
segmentation algorithms to process images with heterogeneous characteristics,
simulating the dynamics of a non-cooperative environment. The accuracy of the
four selected methodologies on the UBIRIS database is tested and, having
concluded about their weak robustness when dealing with non-optimal images
regarding focus, reflections, brightness or eyelid obstruction, the authors
introduce a new and more robust iris image segmentation methodology. This new
methodology could contribute to the aim of non-cooperative biometric iris
recognition, where the ability to process this type of image is required.

### Segmenting the Periocular Region using a Hierarchical Graphical Model Fed
by Texture / Shape Information and Geometrical Constraints [68]

Using the periocular region for biometric recognition is an interesting
possibility: this area of the human body is highly discriminative among
subjects and relatively stable in appearance. In this paper, the main idea is
that improved solutions for defining the periocular region-of-interest and
better pose / gaze estimates can be obtained by segment- ing (labelling) all
the components in the periocular vicinity. Accordingly, authors describe an
integrated algorithm for labelling the periocular region, that uses a unique
model to discriminate between seven components in a single-shot: iris, sclera,
eyelashes, eyebrows, hair, skin and glasses. The descibed solution fuses
texture / shape descriptors and geometrical constraints to feed a two-layered
graphical model (Markov Random Field), which energy minimization provides a
robust solution against uncontrolled lighting conditions and variations in
subjects pose and gaze.

### Iris Recognition: A Method To Segment Visible Wavelength Iris Images
Acquired On-The-Move and At-A-Distance [28]

The main point of this paper is to give a process suitable for the automatic
segmentation of iris images captured at the visible wavelength, on-the-move
and within a large range of image acquisition distances (between 4 and 8
meters). The experiments were performed on images of the UBIRIS.v2 database
and show the robustness of the proposed method to handle the types of non-
ideal images resultant of the aforementioned less constrained image
acquisition conditions.

### A Method for the Identification of Noisy Regions in Normalized Iris Images
[40]

This paper proposes a new method for the identification of noisy regions in
normalized iris images. Starting from a normalized and dimensionless iris
image in the polar coordinate system, the goal is the classification of every
pixel as noise or not noise . This classification could be helpful in the
posterior feature extraction or feature comparison stages regarding the
construction of biometric iris signatures more robust to noise. They propose
the extraction of 8 well known features for each pixel of the images followed
by the classification through a neural network.

### A Method for the Identification of Inaccuracies in the Pupil Segmentation
[41]

This paper analyzes the relationship between the accuracy of the segmentation
algorithm and the error rates of typical iris recognition systems. Authors
selected 1000 images from the UBIRIS database that the segmentation algorithm
can accurately segment and artificially introduced segmentation inaccuracies.
authors repeated the recognition tests and concluded about the strong
relationship between the errors in the pupil segmentation and the overall
false reject rate. Based on this fact, they propose a method to identify these
inaccuracies.

##  IV General Works

### Visual Surveillance and Biometrics: Practices, Challenges, and
Possibilities [3]

Visual surveillance is the latest paradigm of monitoring public security
through machine intelligence. It includes the use of visual data captured by
IR sensors, cameras placed in car, corridors, traffic signals etc. Visual
surveillance facilitates the classification of human behavior, crowd activity,
and gesture analysis to achieve application-specific objectives. Biometrics is
the science of uniquely identifying or verifying an individual among a set of
people by exploring the user s physiological or behavioral characteristics.
Due to their ease of use in many application scenarios (including time
attendance systems, border control, access control for high security, etc.),
biometric systems are currently being introduced in many day-to-day
activities. Sometimes, algorithms developed for visual surveillance systems
have been applied to biometric identification. Recently, several research
efforts have been devoted to merge these two technologies especially for
adverse and covert scenarios. This Special Section in IEEE Access served as a
cross-platform to cover the recent advancements at the intersection of visual
surveillance and biometrics .

### IEEE Intelligent Systems: Trends and Controversies [57]

Performing covert biometric recognition in surveillance environments has been
regarded as a grand challenge, considering the adversity of the conditions
where recognition should be carried out (e.g., poor resolution, bad lighting,
off-pose and partially occluded data). This special issue compiles a group of
approaches to this problem.

### Biometric Recognition in Surveillance Scenarios: A Survey [18]

The interest in the security of individuals has increased in recent years.This
increase has in turn led to much wider deployment of surveillance cameras
worldwide, and con- sequently, automated surveillance systems research has
received more attention from the scientific community than before.
Concurrently, biometrics research has become more popular as well, and it is
supported by the increasing number of approaches devised to address specific
degradation factors of unconstrained environments. Despite these recent
efforts, no automated surveillance system that performs reliable biometric
recognition in such an environment has become available. Nevertheless, recent
developments in human motion analysis and biometric recognition suggest that
both can be combined to develop a fully automated system. As such, this paper
reviews recent advances in both areas, with a special focus on surveillance
scenarios. When compared to previous studies, authors highlight two distinct
features, i.e., (1) put the emphasis is on approaches that are devised to work
in unconstrained environments and surveillance scenarios; and (2) biometric
recognition is the final goal of the surveillance system, as opposed to
behavior analysis, anomaly detection or action recognition.

### Fusing Vantage Point Trees and Linear Discriminants for Fast Feature
Classification [60]

This paper describes a classification strategy that can be regarded as a more
general form of nearest-neighbor classification. It fuses the concepts of
nearest neighbor, linear discriminant and Vantage-Point trees, yielding an
efficient indexing data structure and classification algorithm. In the
learning phase, authors define a set of disjoint subspaces of reduced
complexity that can be separated by linear discriminants, ending up with an
ensemble of simple (weak) classifiers that work locally. In classification,
the closest centroids to the query determine the set of classifiers
considered, which responses are weighted. The algorithm was experimentally
validated in datasets widely used in the field, attaining error rates that are
favorably comparable to the state-of-the-art classification techniques.
Lastly, the proposed solution has a set of interesting properties for a broad
range of applications: 1) it is deterministic; 2) it classifies in time
approximately logarithmic with respect to the size of the learning set, being
far more efficient than nearest neighbor classification in terms of
computational cost; and 3) it keeps the generalization ability of simple
models.

### Editorial of the Special Issue On the Recognition of Visible Wavelength
Iris Images Captured At-a-distance and On-the-move [51]

This special issue regards the recognition of degraded iris images acquired in
visible wavelengths. During 2009 and 2010, the University of Beira Interior
(Portugal) promoted two International evaluation initiatives about this
subject, named Noisy Iris Challenge Evaluation (NICE) I and II. The first one
focussed on the evaluation of iris segmentation strategies, considering that
iris data acquired in visible wave- lengths (VW) usually has much higher level
of detail than traditionally used near infra-red data (NIR), but also has many
more noise arti- facts, including specular and diffuse reflections and
shadows. Also, the spectral reflectance of the sclera is significantly higher
in the VW than in the NIR and the spectral radiance of the iris with respect
to the levels of its pigmentation varies much more significantly in the VW
than in the NIR.

### Introduction to the Special Issue on Unconstrained Biometrics: Advances
and Trends [69]

To date, no research effort has produced a machine able to autonomously and
covertly perform reliable recognition of human beings. Perhaps, contrary to
popular belief, such automata are confined to science fiction, although it is
not hard to anticipate the potential impact that they would have in modern
societies (e.g., forensics and surveillance). Some of the biological traits
used to perform biometric recognition support contactless data acquisition and
can be imaged covertly. Thus, at least theoretically, the subsequent biometric
recognition procedure can be performed without subjects knowledge and in
uncontrolled scenarios. This real-world scenario brings many challenges to the
Pattern Recognition process, essentially due to poor quality of the acquired
data. The feasibility of this type of recognition has received increasing
attention and is of particular interest in visual surveillance, computer
forensics, threat assessment, and other security areas.

### Introduction to the Special Issue on the Segmentation of Visible
Wavelength Iris Images Captured At-a-distance and On-the-move [48]

Deployed iris recognition systems are mainly based on Daugman s pioneering
approach, and have proven their effectiveness in relatively constrained
scenarios: operating in the near infra-red spectrum (NIR, 700-900 nm), at
close acquisition distances and with stop-and-stare interfaces. However, the
human iris supports contactless data acquisition, and it can - at least
theoretically - be imaged covertly. The feasibility of covert iris recognition
receives increasing attention and is of particular interest for forensic and
security purposes. In this scope, one possibility is the use of visible
wavelength light (VW) to perform image acquisition, although the use of this
type of light can severely degrade the quality of the captured data. This is
mainly due to the optical properties of the two molecules that constitute the
pigment of the human iris: brown-black Eumelanin (over 90%) and yellow-reddish
Pheomelanin. Eumelanin has most of its radiative fluorescence under VW, which
enables the capturing of a much higher level of detail, but also of many more
noisy artefacts, including specular and diffuse reflections and shadows. Also,
the spectral reflectance of the sclera is significantly higher in the VW than
in the NIR and the spectral radiance of the iris in respect to the levels of
its pigmentation varies much more significantly in the VW than in the NIR.
Furthermore, traditional template- and boundary-based iris segmentation
approaches will probably fail, due to difficulties in detecting edges or in
fitting rigid shapes. All these reasons justify the need of specialized
segmentation strategies and were the major motivations behind

### Non-Cooperative Iris Recognition: Issues and trends [34]

To date, no research effort has produced a machine able to covertly recognize
human beings. Contrary to popular belief, such automata are confined to
science fiction, although it is not hard to anticipate the potential impact
that they would have in the security and safety of modern societies (forensics
and surveillance). Among the research programs that pursuit such type of
biometric recognition, previous initiatives sought to acquire data from moving
subjects, at long distances and under uncontrolled lighting conditions. This
real- world scenario brings many challenges to the Pattern Recognition
process, essentially due to poor quality of the acquired data. Several
programs now seek to increase the robustness to noise of each phase of the
recognition process (detection, segmentation, normalization, encoding and
matching). This paper addresses the feasibility of such extremely ambitious
type of biometric recognition, discusses the major issues be- hind the
development of this technology and points some directions for further
improvements.

### On the Feasibility of the Visible Wavelength, At-A-Distance and On-The-
Move Iris Recognition [29]

The dramatic growth in practical applications for iris biometrics has been
accompanied by relevant developments in the underlying algorithms and
techniques. Among others, one active research area concerns about the
development of iris recognition systems less constrained to users, either
increasing the imaging distances, simplifying the acquisition protocols or the
required lighting conditions. In this paper authors address the possibility of
perform reliable recognition using visible wavelength images captured under
high heterogeneous lighting conditions, with subjects at-a-distance (between 4
and 8 meters) and on-the-move. The feasibility of this extremely ambitious
type of recognition is analyzed, its major obstacles and challenges discussed
and some directions for forthcoming work pointed.

##  V Deep Learning

### Deep representations for cross-spectral ocular biometrics [79]

One of the major challenges in ocular biometrics is the cross-spectral
scenario, i.e., how to match images acquired in different wavelengths
(typically visible (VIS) against near-infrared (NIR)). This article designs
and extensively evaluates cross-spectral ocular verification methods, for both
the closed and open-world settings, using well known deep learning
representations based on the iris and periocular regions. Using as inputs the
bounding boxes of non-normalized iris/periocular regions, authors fine-tune
Convolutional Neural Network(CNN) models (based either on VGG16 or ResNet-50
architectures), originally trained for face recognition. Based on the
experiments carried out in two publicly available cross-spectral ocular
databases, authors report results for intra-spectral and cross-spectral
scenarios, with the best performance being observed when fusing ResNet-50 deep
representations from both the periocular and iris regions. When compared to
the state-of-the-art, authors observed that the proposed solution consistently
reduces the Equal Error Rate(EER) values by 90% / 93% / 96% and 61% / 77% /
83% on the cross-spectral scenario and in the PolyU Bi-spectral and Cross-eye-
cross-spectral datasets. Lastly, authors evaluate the effect that the
”deepness” factor of feature representations has in recognition effectiveness,
and - based on a subjective analysis of the most problematic pairwise
comparisons - authors point out further directions for this field of research.

### ”A Reminiscence of Mastermind : Iris/Periocular Biometrics by In-Set CNN
Iterative Analysis [65]

Convolutional neural networks (CNNs) have emerged as the most popular
classification models in biometrics research. Under the discriminative
paradigm of pattern recognition, CNNs are used typically in one of two ways:
1) verification mode ( are samples from the same person? ), where pairs of
images are provided to the network to distinguish between genuine and impostor
instances; and 2) identification mode ( whom is this sample from? ), where
appropriate feature representations that map images to identities are found.
This paper postulates a novel mode for using CNNs in biometric identification,
by learning models that answer to the question is the query s identity among
this set? . The insight is a reminiscence of the classical Mastermind game: by
iteratively analysing the network responses when multiple random samples of k
gallery elements are compared to the query, authors obtain weakly correlated
matching scores that - altogether - provide solid cues to infer the most
likely identity. In this setting, identification is regarded as a variable
selection and regularization problem, with sparse linear regression techniques
being used to infer the matching probability with respect to each gallery
identity. As main strength, this strategy is highly robust to outlier matching
scores, which are known to be a primary error source in biometric recognition.
Our experiments were carried out in full versions of two well known irises
near-infrared (CASIA-IrisV4-Thousand) and periocular visible wavelength
(UBIRIS.v2) datasets, and confirm that recognition performance can be solidly
boosted-up by the proposed algorithm, when compared to the traditional working
modes of CNNs in biometrics.

### Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep
Learning Frameworks [64]

This work is based on a disruptive hypothesis for periocular biometrics: in
visible-light data, the recognition performance is optimized when the
components inside the ocular globe (the iris and the sclera) are simply
discarded, and the recogniser s response is exclusively based in information
from the surroundings of the eye. As major novelty, authors describe a
processing chain based on convolution neural networks (CNNs) that defines the
regions-of-interest in the input data that should be privileged in an implicit
way, i.e., without masking out any areas in the learning/test samples. By
using an ocular segmentation algorithm exclusively in the learning data,
authors separate the ocular from the periocular parts. Then, authors produce a
large set of multi-class artificial samples, by interchanging the periocular
and ocular parts from different subjects. These samples are used for data
augmentation purposes and feed the learning phase of the CNN, always
considering as label the ID of the periocular part. This way, for every
periocular region, the CNN receives multiple samples of different ocular
classes, forcing it to conclude that such regions should not be considered in
its response. During the test phase, samples are provided without any
segmentation mask and the network naturally disregards the ocular components,
which contributes for improvements in performance.

### Pose Switch-Based Convolutional Neural Network for Clothing Analysis in
Visual Surveillance Environments [1]

Recognizing pedestrian clothing types and styles in outdoor scenes and totally
uncontrolled conditions is appealing to emerging applications such as
security, intelligent customer profile analysis and computer-aided fashion
design. Recognition of clothing categories from videos remains a challenge,
mainly due to the poor data resolution and the data covariates that compromise
the effectiveness of automated image analysis techniques (e.g., poses, shadows
and partial occlusions). While state-of-the-art methods typically analyze
clothing attributes without paying attention to variation of human poses, here
authors claim for the importance of a feature representation derived from
human poses to improve classification rate. Estimating the pose of pedestrians
is important to fed guided features into recognizing system. In this paper,
authors introduce pose switch-based convolutional neural network for
recognizing the types of clothes of pedestrians, using data acquired in
crowded urban environments. In particular, authors compare the effectiveness
attained when using CNNs without respect to human poses variant, and assess
the improvements in performance attained by pose feature extraction. The
observed results enable us to conclude that pose information can improve the
performance of clothing recognition system. authors focus on the key role of
pose information in pedestrian clothing analysis, which can be employed as an
interesting topic for further works.

### FaceGenderID: Exploiting Gender Information in DCNNs Face Recognition
Systems [6]

This paper addresses the effect of gender as a covariate in face verification
systems. Even though pre-trained models based on Deep Convolutional Neural
Networks (DCNNs), such as VGG-Face or ResNet-50, achieve very high
performance, they are trained on very large datasets comprising millions of
images, which have biases regarding demographic aspects like the gender and
the ethnicity among others. In this work, authors first analyse the separate
performance of these state-of-the-art models for males and females. authors
observe a gap between face verification performances obtained by both gender
classes. These results suggest that features obtained by biased models are
affected by the gender covariate. authors propose a gender-dependent training
approach to improve the feature representation for both genders, and develop
both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model.
Our results show significant and consistent improvements in face verification
performance for both genders, individually and in general with our proposed
approach. Finally, authors announce the availability (at GitHub) of the
FaceGenderID DCNNs models proposed in this work, which can support further
experiments on this topic.

### Segmentation-less and Non-holistic Deep-Learning Framework for Iris
Recognition [66]

Driven by the pioneer iris biometrics approach, the most relevant recognition
methods published over the years are phase-based , and segment/normalize the
iris to obtain dimensionless representations of the data that attenuate the
differences in scale, translation, rotation and pupillary dilation. In this
paper authors present a recognition method that dispenses the iris
segmentation, noise detection and normalization phases, and is agnostic to the
levels of pupillary dilation, while maintaining state-of-the-art performance.
Based on deep-learning classification models, authors anayze the displacements
between biologically corresponding patches in pairs of iris images, to
discriminate between genuine and impostor comparisons. Such corresponding
patches are firstly learned in the normalized representations of the irises -
the domain where they are optimally distinguishable - but are remapped into a
segmentation-less polar coordinate system that uniquely requires iris
detection. In recognition time, samples are only converted into this
segmentation-less coordinate system, where matching is performed.

##  VI Recognition Works

### ”A Leopard Cannot Change Its Spots”: Improving Face Recognition Using
3D-based Caricatures [22]

Caricatures refer to a representation of a person in which the distinctive
features are deliberately exaggerated, with several studies showing that
humans perform better at recognizing people from caricatures than using
original images. Inspired by this observation, this paper introduces the first
fully automated caricature-based face recognition approach capable of working
with data acquired in the wild. Our approach leverages the 3D face structure
from a single 2D image and compares it to a reference model for obtaining a
compact representation of face features deviations. This descriptor is
subsequently deformed using a mea- sure locally, weight globally strategy to
resemble the caricature drawing process. The deformed deviations are
incorporated in the 3D model using the Laplacian mesh deformation algorithm,
and the 2D face caricature image is obtained by projecting the deformed model
in the original camera-view. To demonstrate the advantages of caricature-based
face recognition, authors train the VGG-Face network from scratch using either
original face images (baseline) or caricatured images, and use these models
for extracting face descriptors from the LFW, IJB-A and MegaFace datasets. The
experiments show an increase in the recognition accuracy when using
caricatures rather than original images.

### Joint Head Pose / Soft Label Estimation for Human Recognition In-The-Wild
[56]

Soft biometrics have been emerging to complement other traits and are
particularly useful for poor quality data. In this paper, authors propose an
efficient algorithm to estimate human head poses and to infer soft biometric
labels based on the 3D morphology of the human head. Starting by considering a
set of pose hypotheses, authors use a learning set of head shapes synthesized
from anthropometric surveys to derive a set of 3D head centroids that
constitutes a metric space. Next, representing queries by sets of 2D head
landmarks, authors use projective geometry techniques to rank efficiently the
joint 3D head centroids / pose hypotheses according to their likelihood of
matching each query. The rationale is that the most likely hypotheses are
sufficiently close to the query, so a good solution can be found by convex
energy minimization techniques. Once a solution has been found, the 3D head
centroid and the query are assumed to have similar morphology, yielding the
soft label. Our experiments point toward the usefulness of the proposed
solution, which can improve the effectiveness of face recognizers and can also
be used as a privacy-preserving solution for biometric recognition in public
environments.

### Robust periocular recognition by fusing sparse representations of color
and geometry information [12]

In this paper, authors propose a re-weighted elastic net (REN) model for
biometric recognition. The new model is applied to data separated into
geometric and color spatial components. The geometric information is extracted
using a fast cartoon - texture decomposition model based on a dual formulation
of the total variation norm allowing us to carry information about the overall
geometry of images. Color components are defined using linear and nonlinear
color spaces, namely the red-green-blue (RGB), chromaticity- brightness (CB)
and hue-saturation-value (HSV). Next, according to a Bayesian fusion-scheme,
sparse representations for classification purposes are obtained. The scheme is
numerically solved using a gradient projection (GP) algorithm. In the
empirical validation of the proposed model, authors have chosen the periocular
region, which is an emerging trait known for its robustness against low
quality data.

### Aperiodic Feature Representation for Gait Recognition in Cross-view
Scenarios for Unconstrained Biometrics [26]

The state-of-the-art gait recognition algorithms require a gait cycle
estimation before the feature extraction and are classified as periodic
algorithms. Their effective- ness substantially decreases due to errors in
detecting gait cycles, which are likely to occur in data acquired in non-
controlled conditions. Hence, the main contributions of this paper are: (1)
propose an aperiodic gait recognition strategy, where features are extracted
without the concept of gait cycle, in case of multi-view scenario; (2) propose
the fusion of the different feature subspaces of aperiodic feature
representations at score level in cross-view scenarios. The experiments were
performed with widely known CASIA Gait database B, which enabled us to draw
the following major conclusions, (1) for multi-view scenarios, features
extracted from gait sequences of varying length have as much discriminating
power as traditional periodic features; (2) for cross-view scenarios, authors
observed an average improvement of 22 % over the error rates of state-of- the-
art algorithms, due to the proposed fusion scheme.

### Ocular Biometrics by Score-Level Fusion of Disparate Experts [36]

The concept of periocular biometrics emerged to improve the robustness of iris
recognition to degraded data. Being a relatively recent topic, most of the
periocular recognition algorithms work in a holistic way, and apply a feature
encoding / matching strategy without considering each biological component in
the periocular area. This not only augments the correlation between the
components in the resulting biometric signature, but also increases the
sensitivity to particular data covariates. The main novelty in this paper is
to propose a periocular recognition ensemble made of two disparate components:
1) one expert analyses the iris texture and exhaustively exploits the multi-
spectral information in visible-light data; 2) another expert parameterises
the shape of eyelids and defines a surrounding dimensionless region-of-
interest, from where statistics of the eyelids, eyelashes and skin wrinkles /
furrows are encoded. Both experts work on disjoint regions of the periocular
area and meet three important properties: 1) they produce practically
independent responses, which is behind the better performance of the ensemble
when compared to the best individual recogniser; 2) they don t share
particularly sensitivity to any image covariate, which accounts for augmenting
the robustness against degraded data.

### Face Recognition: Handling Data Misalignments Implicitly by Fusion of
Sparse Representations [55]

Sparse representations for classification (SRC) are considered a relevant
advance to the biometrics field, but are particularly sensitive to data
misalignments. In previous studies, such misalignments were compensated for by
finding appropriate geometric transforms between the elements in the
dictionary and the query image, which is costly in terms of computational
burden. This study describes an algorithm that compensates for data
misalignments in SRC in an implicit way, that is, without finding/applying any
geometric transform at every recognition attempt. The authors study is based
on three concepts: (i) sparse representations; (ii) projections on orthogonal
subspaces; and (iii) discriminant locality preserving with maximum margin
projections. When compared with the classical SRC algorithm, apart from
providing slightly better performance, the proposed method is much more robust
against global/local data misalignments. In addition, it attains performance
close to the state-of-the-art algorithms at a much lower computational cost,
offering a potential solution for real- time scenarios and large-scale
applications.

### Periocular Biometrics: Constraining the EGM Algorithm to Biologically
Plausible Distortions [54]

In biometrics research, the periocular region has been regarded as an
interesting trade-off between the face and the iris, particularly in
unconstrained data acquisition setups. As in other biometric traits, the
current challenge is the development of more robust recognition algorithms.
Having investigated the suitability of the elastic graph matching (EGM)
algorithm to handle non- linear distortions in the periocular region because
of facial expressions, the authors observed that vertices locations often not
correspond to displacements in the biological tissue. Hence, they propose a
globally coherent variant of EGM (GC-EGM) that avoids sudden local angular
movements of vertices while maintains the ability to faithfully model non-
linear distortions. Two main adaptations were carried out: (i) a new term for
measuring vertices similarity and (ii) a new term in the edges-cost function
penalises changes in orientation between the model and test graphs.

### Compensating for Pose and Illumination in Unconstrained Periocular
Biometrics [25]

In the context of less constrained biometrics recognition, the use of
information from the vicinity of the eyes (periocular) is considered with high
potential and motivated several recent proposals. In this paper, authors focus
on two factors that are known to degrade the performance of periocular
recognition: varying illumination conditions and subjects pose. Hence, this
paper has three major purposes: 1) describe the decreases in performance due
to varying illumination and subjects poses; 2) propose two techniques to
improve the robustness to these factors; 3) announce the availability of an
annotated dataset of periocular data (UBIPosePr), where poses vary in regular
intervals, turning it especially suitable to assess the effects of
misalignments between camera and subjects in periocular recognition.

### Fusing Color and Shape Descriptors in the Recognition of Degraded Iris
Images Acquired at Visible Wavelength [67]

Despite the substantial research into the development of covert iris
recognition technologies, no machine to date has been able to reliably perform
recognition of human beings in real-world data. This limitation is especially
evident in the application of such technology to large-scale identification
scenarios, which demand extremely low error rates to avoid frequent false
alarms. Most previously published works have used intensity data and performed
multi-scale analysis to achieve recognition, obtaining encouraging performance
values that are nevertheless far from desirable. This paper presents two key
innovations. (1) A recognition scheme is proposed based on techniques that are
substantially different from those traditionally used, starting with the
dynamic partition of the noise-free iris into disjoint regions from which
MPEG-7 color and shape descriptors are extracted. (2) The minimal levels of
linear correlation between the outputs produced by the proposed strategy and
other state-of-the-art techniques suggest that the fusion of both recognition
techniques significantly improve performance, which is regarded as a positive
step towards the development of extremely ambitious types of biometric
recognition.

### An Iris Recognition Approach Through Structural Pattern Analysis Methods
[32]

Continuous efforts have been made in searching for robust and effective iris
cod- ing methods, since Daugman s pioneering work on iris recognition was
published. Proposed algorithms follow the statistical pattern recognition
paradigm and encode the iris texture in- formation through phase, zero-
crossing or texture-analysis based methods. In this paper authors propose an
iris recognition algorithm that follows the structural (syntactic) pattern
recognition paradigm, which can be advantageous essentially for the purposes
of description and of the human-perception of the system s functioning. Our
experiments, that were performed on two widely used iris image databases
(CASIA.v3 and ICE), show that the proposed iris structure provides enough
discriminating information to enable accurate biometric recognition, while
maintains the advantages intrinsic to structural pattern recognition systems.

### Toward Non-Cooperative Iris Recognition: A Classification Approach Using
Multiple Signatures [44]

This paper focus on noncooperative iris recognition, i.e., the capture of iris
images at large distances, under less controlled lighting conditions, and
without active participation of the subjects. This increases the probability
of capturing very heterogeneous images (regarding focus, contrast, or
brightness) and with several noise factors (iris obstructions and
reflections). Current iris recognition systems are unable to deal with noisy
data and substantially increase their error rates, especially the false
rejections, in these conditions. authors propose an iris classification method
that divides the segmented and normalized iris image into six regions, makes
an independent feature extraction and comparison for each region, and combines
each of the dissimilarity values through a classification rule.

### Region-Based CNNs for Pedestrian Gender Recognition in Visual Surveillance
Environments [78]

Inferring soft biometric labels in totally uncontrolled outdoor environments,
such as surveillance scenarios, remains a challenge due to the low resolution
of data and its covariates that might seriously compromise performance (e.g.,
occlusions and subjects pose). In this kind of data, even state-of-the-art
deep-learning frameworks (such as ResNet) working in a holistic way, attain
relatively poor performance, which was the main motivation for the work
described in this paper. In particular, having noticed the main effect of the
subjects’ pose factor, in this paper authors describe a method that uses the
body keypoints to estimate the subjects pose and define a set of regions of
interest (e.g., head, torso, and legs). This information is used to learn
appropriate classification models, specialized in different poses/body parts,
which contributes to solid improvements in performance. This conclusion is
supported by the experiments authors conducted in multiple real-world outdoor
scenarios, using the data acquired from advertising panels placed in crowded
urban environments.

### IRINA: Iris Recognition (even) in Inacurately Segmented Data [61]

The effectiveness of current iris recognition systems de- pends on the
accurate segmentation and parameterisation of the iris boundaries, as failures
at this point misalign the coefficients of the biometric signatures. This
paper de- scribes IRINA, an algorithm for Iris Recognition that is ro- bust
against INAccurately segmented samples, which makes it a good candidate to
work in poor-quality data. The pro- cess is based in the concept of
corresponding patch be- tween pairs of images, that is used to estimate the
posterior probabilities that patches regard the same biological region, even
in case of segmentation errors and non-linear texture deformations. Such
information enables to infer a free-form deformation field (2D registration
vectors) between images, whose first and second-order statistics provide
effective bio- metric discriminating power.

### Quis-Campi: Extending In The Wild Biometric Recognition to Surveillance
Environments [77]

Efforts in biometrics are being held into extending robust recognition
techniques to in the wild scenarios. Nonetheless, and despite being a very
attractive goal, human identification in the surveillance con- text remains an
open problem. In this paper, authors introduce a novel biometric system Quis-
Campi that effectively bridges the gap between surveillance and biometric
recognition while having a minimum amount of operational restrictions. authors
propose a fully automated surveillance sys- tem for human recognition
purposes, attained by combining human detection and tracking, further enhanced
by a PTZ camera that delivers data with enough quality to perform biometric
recognition. Along with the system concept, implementation details for both
hardware and software modules are provided, as well as preliminary results
over a real scenario

### Robust Periocular Recognition by Fusing Local to Holistic Sparse
Representations [11]

Sparse representations have been advocated as a relevant advance in biometrics
research. In this paper authors propose a new algorithm for fusion at the data
level of sparse representations, each one obtained from image patches. The
main novelties are two-fold: 1) a dictionary fusion scheme is formalised,
using the l1minimization with the gradient projection method; 2) the proposed
representation and classification method does not require the non-overlapping
condition of image patches from where individual dictionaries are obtained. In
the experiments, authors focused in the recognition of periocular images and
obtained independent dictionaries for the eye, eyebrow and skin regions, that
were subsequently fused. Results obtained in the publicly available UBIRIS.v2
data set show consistent improvements in the recognition effectiveness when
compared to state-of-the-art related representation and classification
techniques.

### Periocular Biometrics: An Emerging technology for Unconstrained Scenarios
[75]

The periocular region has recently emerged as a promising trait for
unconstrained biometric recognition, specially on cases where neither the iris
and a full facial picture can be obtained. Previous studies concluded that the
regions in the vicinity of the human eye - the periocular region- have
surprisingly high discriminating ability between individuals, are relatively
permanent and easily acquired at large distances. Hence, growing attention has
been paid to periocular recognition methods, on the performance levels they
are able to achieve, and on the correlation of the responses given by other.
This work overviews the most relevant research works in the scope of
periocular recognition: summarizes the developed methods, and enumerates the
current issues, providing a comparative overview. For contextualization, a
brief overview of the biometric field is also given.

### Iris Recognition: A Method to Increase the Robustness to Noisy Imaging
Environments Through the Selection of the Higher Discriminating Features [46]

Continuous efforts have been made in searching for robust and effective iris
coding methods, since Daugman s pioneering work on iris recognition was
published. However, due to lack of robustness, the error rates of iris
recognition systems significantly increase when images contain large portions
of noise (reflections and iris obstructions), resultant from less constrained
imaging conditions. Current iris encoding and matching proposals do not take
into account the specific lighting conditions of the imaging environment,
decreasing their adaptability to such dynamics conditions. In this paper
authors propose a method that, through a learning stage, takes into account
the typical noisy regions propitiated by the imaging environment to select the
higher discriminating features.

### Iris Recognition: An Entropy-Based Coding Strategy Robust to Noisy Imaging
Environments [45]

The iris is currently accepted as one of the most accurate traits for
biometric purposes. However, for the sake of accuracy, iris recognition
systems rely on good quality images and significantly deteriorate their
results when images contain large noisy regions, either due to iris
obstructions (eyelids or eyelashes) or reflections (specular or lighting). In
this paper authors propose an entropy-based iris coding strategy that
constructs an unidimensional signal from overlapped angular patches of
normalized iris images. Further, in the comparison between biometric
signatures authors exclusively take into account signatures segments of
varying dimension. The hope is to avoid the comparison between components
corrupted by noise and achieve accurate recognition, even on highly noisy
images. Our experiments were performed in three widely used iris image
databases (third version of CASIA, ICE and UBIRIS) and led us to observe that
our proposal significantly decreases the error rates in the recognition of
noisy iris images.

### A Structural Pattern Analysis Approach to Iris Recognition [27]

Continuous efforts have been made in searching for robust and effective iris
cod- ing methods, since Daugman s pioneering work on iris recognition was
published. Proposed algorithms follow the statistical pattern recognition
paradigm and encode the iris texture in- formation through phase, zero-
crossing or texture-analysis based methods. In this paper authors propose an
iris recognition algorithm that follows the structural (syntactic) pattern
recognition paradigm, which can be advantageous essentially for the purposes
of description and of the human-perception of the system s functioning. Our
experiments, that were performed on two widely used iris image databases
(CASIA.v3 and ICE), show that the proposed iris structure provides enough
discriminating information to enable accurate biometric recognition, while
maintains the advantages intrinsic to structural pattern recognition systems.

### Iris Recognition: Measuring Feature s Quality for the Feature Selection in
Unconstrained Image Capture Environments [42]

Iris recognition has been used for several purposes. However, current iris
recognition systems are unable to deal with noisy data and substantially
increase their error rates, specially the false rejections, in these
conditions. Several proposals have been made to access image quality and to
identify noisy regions in iris images. In this paper authors propose a method
that measures the quality of each feature of the biometric signature and takes
account into this information to constraint the comparable features and obtain
the similarity between iris signatures. Experiments led us to conclude that
this method significantly decreases the error rates in the recognition of
noisy iris images, resultant from capturing in less constrained environments.

##  VII Empirical Works

### Experiments with Ocular Biometric Datasets: A Practitioner s Guideline [2]

Ocular biometrics is the imaging and use of features extracted from the eyes
regions for personal recognition. Ocular biometrics is a promising research
field owing to factors such as recognition at a distance and suitability for
recognition with regular RGB cameras, especially in visible spectrum on mobile
devices. To ensure that ocular biometric academic researches have a positive
impact on future technological developments, this paper provides a review of
ocular databases available in litera- ture, diversities among these databases,
design and parameters consideration issues during acquisition of database and
selection of appropriate database for experimentation. Open issues and future
research directions are also discussed to identify the path forward.

### Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
[9]

Mobile biometrics technologies are nowadays the new frontier for secure use of
data and services, and are considered particularly important due to the
massive use of handheld devices in the entire world. Among the biometric
traits with potential to be used in mobile settings, the iris/ocular region is
a natural candidate, even considering that further advances in the technology
are required to meet the operational requirements of such ambitious
environments. Aiming at promoting these advances, authors organized the Mobile
Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison
of the performance of the participant methods by various Figures of Merit
(FoMs). A particular attention is devoted to the identification of the image
covariates that are likely to cause a decrease in the performance levels of
the compared algorithms. Among these factors, interoperability among different
devices plays an important role. The methods (or parts of them) implemented by
the analyzed approaches are classified into segmentation (S), which was the
main target of MICHE-I, and recognition (R). The paper reports both the
results observed for either S or R, and also for different recombinations
(S+R) of such methods. Last but not least, authors also present the results
obtained by multi-classifier strategies.

### Results from MICHE II - Mobile Iris CHallenge Evaluation II [10]

Mobile biometrics represent the new frontier of authentication. The most
appealing feature of mobile devices is the wide availability and the presence
of more and more reliable sensors for capturing bio- metric traits, e.g.,
cameras and accelerometers. Moreover, they more and more often store personal
and sensitive data, that need to be protected. Doing this on the same device
using biometrics to enforce security seems a natural solution. This makes this
research topic attracting and generally promising. However, the growing
interest for related applications is counterbalanced by still present
limitations, especially for some traits. Acquisition and computation resources
are nowadays widely available, but they are not al- ways sufficient to allow a
reliable recognition result. Most of all, the way capture is expected to be
carried out, i.e., by the user him/herself in uncontrolled conditions and
without an expert assistance, can heavily affect the quality of samples and,
as a consequence, the accuracy of recognition. Among the biometric traits
raising the interest of researchers, iris plays an important role. Mobile Iris
CHallenge Evaluation II (MICHE II) competition provided a testbed to assess
the progress of mobile iris recognition, as well as its limitations still to
overcome. This paper presents the results of the competition and the analysis
of achieved performance, that takes into account both proposals submitted for
the competition section launched at the 2016 edition of the International
Conference on Pattern Recognition (ICPR), as well as proposals submitted for
this special issue.

### Periocular Recognition: How Much Facial Expressions Affect Performance?
[5]

Using information near the human eye to per- form biometric recognition has
been gaining popularity. Previous works in this area, designated periocular
recognition, show remarkably low error rates and particularly high robustness
when data are acquired under less con- trolled conditions. In this field, one
factor that remains to be studied is the effect of facial expressions on
recognition performance, as expressions change the textural/shape information
inside the periocular region. authors have collected a multisession dataset
whose single variation is the subjects facial expressions and analyzed the
corresponding variations in performance, using the state-of-the-art peri-
ocular recognition strategy. The effectiveness attained by different
strategies to handle the effects of facial expressions was compared: (1)
single-sample enrollment; (2) multisample enrollment, and (3) multisample
enrollment with facial expression recognition, with results also vali- dated
in the well-known Cohn-Kanade AU-Coded Expression dataset. Finally, the role
of each type of facial expression in the biometrics menagerie effect is
discussed.

### Mobile Iris CHallenge Evaluation II: results from the ICPR competition.
[8]

The growing interest for mobile biometrics stems from the increasing need to
secure personal data and services, which are often stored or accessed from
there. Modern user mobile devices, with acquisition and com- putation
resources to support related operations, are nowadays widely available. This
makes this research topic very attracting and promising. Iris recognition
plays a major role in this scenario. However, mo- bile biometrics still suffer
from some hindering fac- tors. The resolution of captured images and the com-
putational power are not comparable to desktop sys- tems yet. Furthermore, the
acquisition setting is gener- ally uncontrolled, with users who are not that
expert to autonomously generate biometric samples of sufficient quality.
Mobile Iris CHallenge Evaluation aims at pro- viding a testbed to assess the
progress of mobile iris recognition, and to evaluate the extent of its present
lim- itations. This paper presents the results of the compe- tition launched
at the 2016 edition of the International Conference on Pattern Recognition
(ICPR)

### Iris Recognition: What’s Beyond Bit Fragility? [37]

The concept of fragility of some bits in the iris codes regards exclusively
their within-class variation, i.e., the probability that they take different
values in templates computed from different images of the same iris. This
paper extends that concept, by noticing that a similar phenomenon occurs for
the between-classes comparisons, i.e., some bits have higher probability than
others of assuming a predominant value, which was observed for near-infrared
and (in a more evident way) for visible wavelength data. Accordingly, authors
propose a new measure (bit discriminability) that takes into account both the
within-class and between-classes variabilities, and has roots in the Fisher
discriminant. Based on the bit discriminability, authors compare the
usefulness of the different regions of the iris for biometric recognition,
with respect to multi-spectral data and to different filters
parameterizations. Finally, authors measure the amount of information lost in
codes quantization, which gives insight to further research on iris matching
strategies that consider both phase and magnitude. Albeit augmenting the
computational burden of recognition, such kind of strategies will consistently
improve performance, particularly in poor-quality data.

### Toward Covert Iris Biometric Recognition: Experimental Results From the
NICE Contests [ProencaAlexandre2012b]

This paper announces and discusses the experimental results from the Noisy
Iris Challenge Evaluation (NICE), an iris biometric evaluation initiative that
received worldwide participation and whose main innovation is the use of
heavily degraded data acquired in the visible wavelength and uncontrolled
setups, with subjects moving and at widely varying distances. The NICE contest
included two separate phases: 1) the NICE.I evaluated iris segmentation and
noise detection techniques and 2) the NICE:II evaluated encoding and matching
strategies for biometric signatures. Further, authors give the performance
values observed when fusing recognition methods at the score level, which was
observed to outperform any isolated recognition strategy. These results
provide an objective estimate of the potential of such recognition systems and
should be regarded as reference values for further improvements of this
technology, which - if successful - may significantly broaden the
applicability of iris biometric systems to domains where the subjects cannot
be expected to cooperate.

### Iris Recognition: Analysis of the Error Rates Regarding the Accuracy of
the Segmentation Stage [50]

Iris recognition has been widely used in several scenarios with very
satisfactory results. As it is one of the earliest stages, the image
segmentation is in the basis of the process and plays a crucial role in the
success of the recognition task. In this paper authors analyze the
relationship between the accuracy of the iris segmentation process and the
error rates of three typical iris recognition methods. authors selected 5000
images of the UBIRIS, CASIA and ICE databases that the used segmentation
algorithm can accurately segment and artificially simulated four types of
segmentation inaccuracies. The obtained results allowed us to con- clude about
a strong relationship between translational segmentation inaccuracies - that
lead to errors in phase - and the recognition error rates.

### ICB-RW 2016: International Challenge on Biometric Recognition in the Wild
[19]

Biometric recognition in totally wild conditions, such as the observed in
visual surveillance scenarios has not been achieved yet. The ICB-RW
competition was promoted to support this endeavor, being the first biometric
challenge carried out in data that realistically result from surveillance
scenarios. The competition relied on an innovative master- slave surveillance
system for the acquisition of face imagery at-a-distance and on-the-move. This
paper describes the competition details and reports the performance achieved
by the participants algorithms.

### Evaluation of Background Subtraction Algorithms for Human Visual
Surveillance [17]

The fully automated surveillance of human be- ings remains an open problem,
particularly for in-the-wild scenarios, i.e., for complex backgrounds and
under uncon- trolled lighting conditions. Background Subtraction (BGS) is
typically the first phase of the processing chain of such type of systems and
holds the feasibility of all the subsequent phases. Hence, it is particularly
important to perceive the relative effectiveness of BGS, with respect to the
kind of environment. This paper gives an objective evaluation of the state-of-
the-art BGS algorithms on unconstrained outdoor environments. When compared to
similar published works, the major novelties are two-fold: 1) the focus is put
on scenes populated by human beings; and 2) an objective measure of the
wildness of environments is proposed, that strongly correlates to BGS
performance, and enables to perceive the algorithms robustness with respect to
the environment complexity. As main conclusions, authors observed that the
SOBS algorithm outperforms the remaining methods. Nevertheless, its
performance leads to conclude that BGS in unconstrained environments is still
an open problem.

### Creating Synthetic IrisCodes to Feed Biometrics Experiments [62]

The collection of iris data suitable to be used in experiments is difficult,
mainly due to two factors: 1) the time spent by volunteers in the acquisition
process; and 2) security / privacy concerns of volunteers. Even though there
are methods to create images of artificial irises, there is no method
exclusively focused in the synthesis of the iris biometric signatures
(IrisCodes). In experiments related with some phases of the biometric
recognition process (e.g., indexing / retrieval), a large number of signatures
is required for proper evaluation, which, in case of real data, is extremely
hard to obtain. Hence, this paper describes a stochastic method to synthesize
IrisCodes, based on the notion of data correlation. These artificial
signatures can be used to feed experiments on iris recognition, namely on the
iris matching, indexing and retrieval phases. authors experimentally confirmed
that both the genuine and impostor distributions obtained on the artificial
data closely resemble the values obtained in data sets of real irises.
Finally, another interesting feature is that the method is easily parametrized
to mimic IrisCodes extracted from data of varying levels of quality, i.e.,
ranging from data acquired in high controlled to unconstrained environments.

### Facial Expressions: Discriminability of Facial Regions and Relationship to
Biometrics Recognition [4]

Facial expressions result from movements of mus- cular action units, in
response to internal emotion states or perceptions, and it has been shown that
they decrease the performance of face-based biometric recognition techniques.
This paper focuses in the recognition of facial expressions and has the
following purposes: 1) confirm the suitability of using dense image
descriptors widely known in biometrics research (e.g., local binary patterns
and histograms of oriented gradients) to recognize facial expressions; 2)
compare the effectiveness attained when using different regions of the face to
recognize expressions; 3) compare the effectiveness attained when the identity
of subjects is known / unknown, before attempting to recognize their facial
expressions.

### Periocular Recognition: Analysis of Performance Degradation Factors [24]

Among the available biometric traits such as face, iris and fingerprint, there
is an active research being carried out in the direction of unconstrained
biometrics. Periocular recognition has proved its effectiveness and is
regarded as complementary to iris recognition. The main objectives of this
paper are three-fold: 1) to announce the availability of periocular dataset,
which has a variability in terms of scale change (due to camera-subject
distance), pose variation and non-uniform illumination; 2) to investigate the
performance of periocular recognition methods with the presence of various
degradation factors; 3) propose a new initialization strategy for the
definition of the periocular region-of- interest (ROI), based on the geometric
mean of eye corners. Our experiments confirm that performance can be consis-
tently improved by this initialization method, when com- pared to the
classical technique.

### Iris Recognition: Preliminary Assessment about the Discriminating Capacity
of Visible Wavelength Data [73]

The human iris supports contactless data acquisition and can be imaged
covertly. These factors give raise to the possibility of performing biometric
recognition procedure with- out subjects knowledge and in uncontrolled data
acquisition scenarios. The feasibility of this type of recognition has been
receiving increasing attention, as is of particular interest in visual
surveillance, computer forensics, threat assessment, and other security areas.
In this paper authors stress the role played by the spectrum of the visible
light used in the acquisition process and assess the discriminating iris
patterns that are likely to be acquired according to three factors: type of
illuminant, it s luminance, and levels of iris pigmentation. Our goal is to
perceive and quantify the conditions that appear to enable the biometric
recognition process with enough confidence

### Iris Recognition: Analysing the Distribution of the Iriscodes Concordant
Bits [72]

The growth in practical applications for iris bio- metrics has been
accompanied by relevant developments in the underlying algorithms and
techniques. Efforts are being made to minimize the tradeoff between the
recognition error rates and data quality, acquired in the visible wavelength,
in less controlled environments, over simplified acquisition protocols and
varying lighting conditions. This paper presents an approach that can be
regarded as an extension to the widely known Daugman s method. Its basis is
the analysis of the distribution of the concordant bits when matching
iriscodes on both the spatial and frequency domains. Our experiments show that
this method is able to improve the recognition performance over images
captured in less constrained acquisition setups and protocols. Such conclusion
was drawn upon trials conducted for multiple datasets.

### Biometric Recognition: When Is Evidence Fusion Advantageous? [30]

Having assessed the performance gains due to evidence fusion, previous works
reported contradictory conclusions. For some, a consistent improvement is
achieved, while others state that the fusion of a stronger and a weaker
biometric expert tends to produce worst results than if the best expert was
used individually. The main contribution of this paper is to assess when
improvements in performance are actually achieved, regarding the individual
performance of each expert. Starting from readily satisfied assumptions about
the score distributions generated by a biometric system, authors predict the
performance of each of the individual experts and of the fused system. Then,
authors conclude about the performance gains in fusing evidence from multiple
sources. Also, authors parameterize an empirically obtained relationship
between the individual performance of the fused experts that contributes to
decide whether evidence fusion techniques are advantageous or not.

### On the Role of Interpolation in the Normalization of Non-Ideal Visible
Wavelength Iris Images [71]

The growth in practical applications for iris bio- metrics has been
accompanied by relevant developments in the underlying algorithms and
techniques. Along with the research focused on near-infrared (NIR)
cooperatively captured images, efforts are being made to minimize the trade-
off between the quality of the captured data and the recognition accuracy on
less constrained environments, where images are obtained at the visible
wavelength, at increased distances, over simplified protocols and adverse
lightning. This paper addresses the effect of the interpolation method, used
in the iris normalization stage, in the overall recognition error rates. This
effect is stressed for systems operating under less constrained image
acquisition setups and protocols, due to higher variations in the amounts of
captured data. Our experiments led us to conclude that the utility of the
image interpolating methods is directly corresponding to the levels of noise
that images contain.

### The NICE.I: Noisy Iris Challenge Evaluation - Part I [47]

This paper gives an overview of the NICE.I : Noisy Iris Challenge Evaluation -
Part I contest. This contest differs from others in two fundamental points.
First, instead of the complete iris recognition process, it exclusively
evaluates the iris segmentation and noise detection stages, allowing the
independent evaluation of one of the main recognition error sources. Second,
it operates on highly noisy images that were captured to simulate less
constrained imaging environments and constitute the second version of the
UBIRIS database (UBIRIS.v2).

### Iris Recognition: An Analysis of the Aliasing Problem in the Iris
Normalization Stage [43]

Iris recognition has been increasingly used with very satisfactory results.
Presently, the challenge consists in unconstrain the image capturing
conditions and enable its application to domains where the subjects
cooperation is not expectable (e.g. criminal/terrorist seek, missing
children). In this type of use, due to variations in the image capturing
distance and in the lighting conditions that determine the size of the
subjects pupil, the area correspondent to the iris in the captured images will
be highly varying too. In order to compensate this variation, common iris
recognition proposals translate the segmented iris image to a double
dimensionless pseudo-polar coordinate system, in a process known as the
normalization stage, which can be regarded as a sampling of the original data
with the inherent possibility of aliasing. In this paper authors analyze the
relationship between the size of the captured iris image and the overall
recognition s accuracy. Further, authors identify the threshold for the
sampling rate of the iris normalization process above which the error rates
significantly increase.

##  VIII Other Works

### Quality Assessment of Degraded Iris Images Acquired in the Visible
Wavelength [33]

Data quality assessment is a key issue, in order to broad the applicability of
iris biometrics to unconstrained imaging conditions. Previous research efforts
sought to use visible wavelength (VW) light imagery to acquire data at
significantly larger distances than usual and on moving subjects, which makes
this real world data notoriously different from the acquired in the near
infra-red (NIR) setup. Having empirically observed that published strategies
to assess iris image quality do not handle the specificity of such data, this
paper proposes a method to assess the quality of VW iris samples captured in
unconstrained conditions, according to the factors that are known to determine
the quality of iris biometric data: focus, motion, angle, occlusions, area,
pupillary dilation and levels of iris pigmentation. The key insight is to use
the output of the segmentation phase in each assessment, which permits to
handle severely degraded samples that are likely to result of such imaging
setup. Also, our experiments point that the given method improves the
effectiveness of VW iris recognition, by avoiding that poor quality samples
are considered in the recognition process.

### Exploiting Data Redundancy for Error Detection in Degraded Biometric
Signatures Resulting From in the Wild Environments [20]

An error-correcting code (ECC) is a process of adding redundant data to a
message, such that it can be recovered by a receiver even if a number of
errors are introduced in transmission. Inspired by the principles of ECC,
authors introduce a method capable of detecting degraded features in biometric
signatures by exploiting feature correlation. The main novelty is that, unlike
existing biometric cryptosystems, the proposed method works directly on the
biometric signature. Our approach performs a redundancy analysis of non-
degraded data to build an undirected graphical model (Markov Random Field),
whose energy minimization determines the sequence of degraded components of
the biometric sample. Experiments carried out in different biometric traits
ascertain the improvements attained when disregarding degraded features during
the matching phase. Also, authors stress that the proposed method is general
enough to work in different classification methods, such as CNNs.

### Multimodal Ocular Biometrics Approach: A Feasibility Study [23]

Growing efforts have been concentrated on the development of alternative
biometric recognition strategies, the intended goal to increase the accuracy
and counterfeit-resistance of existing systems without increased cost. In this
paper, authors propose and evaluate a novel biometric approach using three
fundamentally different traits captured by the same camera sensor. Considered
traits include: 1) the internal, non-visible, anatomical properties of the
human eye, represented by Oculomotor Plant Characteristics (OPC); 2) the
visual attention strategies employed by the brain, represented by Complex Eye
Movement pat- terns (CEM); and, 3) the unique physical structure of the iris.
Our experiments, performed using a low-cost web camera, indicate that the
combined ocular traits improve the accuracy of the resulting system. As a
result, the combined ocular traits have the potential to enhance the accuracy
and counterfeit-resistance of existing and future biometric systems.

### A Robust Eye-Corner Detection Method for Real-World Data [74]

Corner detection has been motivating several research works and is
particularly important in different computer vision tasks, acting as basis for
further image understanding stages. Particularly, the detection of eye-corners
in facial images is relevant for domains such as biometric systems and
assisted-driving systems. Having empirically evaluated the state-of-the-art of
eye-corner detection proposals, authors observed that they only achieve
satisfactory results when dealing with good quality data. Hence, in this paper
authors describe an eye- corner detection method with particular focus on
robustness, i.e., the suitability to deal with degraded data, toward the
applicability in real-world conditions. Our experiments show that the proposed
method outperforms others either in noise- free and degraded data (blurred,
rotated and with significant variations in scale), which is regarded as the
main achievement.

### Combining Rectangular and Triangular Image Regions to Perform Real-Time
Face Detection [53]

Nowadays, face detection techniques assume grow- ing relevance in a wide range
of applications (e.g., bio- metrics and automatic surveillance)and constitute
a pre- requisite of many image processing stages. Among a large number of
published approaches, one of the most relevant is the method proposed by Viola
and Jones to perform real-time face detection through a cascade schema of weak
classifiers that act together to compose a strong and robust classifier. This
method was the basis of our work and motivated the key contributions given in
this paper. At first, based on the computer graphics concept of triangle mesh
authors propose the notion of triangular integral feature to describe and
model face properties. Also, authors show results of our face detection
experiments that point to an increase of the detection accuracy when the
triangular features are mixed with the rectangular in the candidate feature
set, which is considered an achievement. Also, it should be stressed that this
optimization is obtained without any relevant increase in the computational
requirements, either spacial or temporal, of the detection method.

### Iris Biometrics: Indexing and Retrieving Heavily Degraded Data [35]

Most of the methods to index iris biometric signa- tures were designed for
decision environments with a clear separation between genuine and impostor
matching scores. However, in case of less controlled data acquisition, images
will be degraded and the decision environments poorly separated. This paper
proposes an indexing / retrieval method for degraded images and operates at
the code level, making it compatible with different feature encoding
strategies. Gallery codes are decomposed at multiple scales, and according to
their most reliable components at each scale, the position in an n-ary tree
determined. In retrieval, the probe is decomposed similarly, and the distances
to multi-scale centroids are used to penalize paths in the tree. At the end,
only a subset of the branches is traversed up to the last level. When compared
to related strategies, the proposed method outperforms them on degraded data,
particularly in the performance range most important for biometrics (hit rates
above 0.95). Finally, according to the computational cost of the retrieval
phase, the number of enrolled identities above which indexing is
computationally cheaper than an exhaustive search is determined.

##  IX Conclusions

The development of biometric recognition solutions able to work in visual
surveillance conditions, i.e., in unconstrained data acquisition conditions
and under covert protocols has been motivating growing efforts from the
research community. Among the various laboratories, schools and research
institutes concerned about this problem, the SOCIA: Soft Computing and Image
Analysis Lab., of the University of Beira Interior, Portugal, has been among
the most active in pursuing disruptive solutions for obtaining such extremely
ambitious kind of automata. This report summarised the research works
published by elements of the SOCIA Lab. in the last decade in the scope of
biometric recognition in unconstrained conditions. Hopefully, it can be used
as a reference for someone entering in this extremely challenging research
topic.

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  * [60] H. Proença and J. C. Neves.  Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification.  _Springer Journal of Classification_ , 34(1), pag. 85-107, doi: 10.1007/s00357-017-9223-0, 2017.
  * [61] H. Proença and J. C. Neves.  IRINA: Iris Recognition (even) in Inacurately Segmented Data  _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2017_ , pag. 6747-6756, Honolulu, Hawai, U.S.A., July 21-26, 2017.
  * [62] H. Proença and J. C. Neves.  Creating Synthetic IrisCodes to Feed Biometrics Experiments.  _Proceedings of the 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications - BioMS 2013_ , pag. 8-13, Napoli, September 9, 2013.
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  * [64] H. Proença and J. C. Neves.  Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning Frameworks.  _IEEE Transactions on Information Forensics and Security_ , 13(4), pag. 888-896, doi: 10.1109/TIFS.2017.2771230, 2018.
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  * [76] G. Santos, P. Fiadeiro and H. Proença  BioHDD: A Dataset for Studying Biometric Identification on Heavily Degraded Data.  _IET Biometrics_ , 4(1), pag. 1-9, 2015.
  * [77] G. Santos, J. C. Neves, S. Filipe, E. Grancho, S. Barra, F. Narducci and H. Proença.  Quis-Campi: Extending In The Wild Biometric Recognition to Surveillance Environments.  _Proceedings of the 18th International Conference on Image Analysis and Processing - ICIAP 2015_ , Genova, Italy, September 7-11, 2015.
  * [78] E. Yaghoubi, P. Alirezazadeh, E. Assunção, J. C. Neves and H. Proença.  Region-Based CNNs for Pedestrian Gender Recognition in Visual Surveillance Environments.  _Proceedings of the 18th International Conference of the Biometrics Special Interest Group - BIOSIG 2019_ , Darmstadt, Germany., September 16-20, 2019.
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