# Synthesizing Iris Images using Generative Adversarial Networks:
Survey and Comparative Analysis

Shivangi Yadav
Michigan State University
<EMAIL_ADDRESS>Arun Ross
Michigan State University
<EMAIL_ADDRESS>
###### Abstract

Biometric systems based on iris recognition are currently being used in border
control applications and mobile devices. However, research in iris recognition
is stymied by various factors such as limited datasets of bonafide irides and
presentation attack instruments; restricted intra-class variations; and
privacy concerns. Some of these issues can be mitigated by the use of
synthetic iris data. In this paper, we present a comprehensive review of
state-of-the-art GAN-based synthetic iris image generation techniques,
evaluating their strengths and limitations in producing realistic and useful
iris images that can be used for both training and testing iris recognition
systems and presentation attack detectors. In this regard, we first survey the
various methods that have been used for synthetic iris generation and
specifically consider generators based on StyleGAN, RaSGAN, CIT-GAN, iWarpGAN,
StarGAN, etc. We then analyze the images generated by these models for
realism, uniqueness, and biometric utility. This comprehensive analysis
highlights the pros and cons of various GANs in the context of developing
robust iris matchers and presentation attack detectors.

Keywords: Generative Adversarial Network (GAN), Synthetic Irides, Deep
Learning, Presentation Attack, Iris Recognition.

##  1 Introduction

Iris based biometric recognition systems have gained significant attention in
recent years with applications in domains such as smartphone access, border
control and airport security [60, 28, 62]. However, as this field advances, it
brings forth a range of research challenges that require exploration and
innovation [69]. Notably, a prominent challenge in biometrics research is the
availability of datasets with sufficient size, quality, and diverse intra-
class variations. Despite significant strides in biometric technology, the
datasets commonly employed for training and evaluating these systems
frequently fall short in terms of sample quantity and the inclusion of a
broader spectrum of intra-class variabilities. This deficiency hinders the
development and dependable evaluation of iris recognition systems. Another
critical challenge is to ensure the privacy of individuals whose biometric
data is used for research purposes [80]. Biometric information, being
inherently personal and unique, raises concerns about unauthorized access,
data breaches, and potential identity theft. Protecting the privacy of
individuals while utilizing their biometric data is of utmost importance to
foster trust and encourage widespread adoption of biometric systems [80].
Researchers have been actively working on finding solutions to overcome these
challenges. One of such solution is to explore the potential of synthetically
generated iris datasets. In fact, synthetic image generation is an active area
of research in the field of fingerprint recognition [85, 25, 96, 78] and face
recognition [61, 32, 35, 26, 79].

Figure 1: Examples of bonafide irides (green box) and synthetic iris images
generated using RaSGAN (red box) [87].

Synthetic iris generation refers to the creation of digital iris images that
emulate the traits and patterns observed in real iris images [19, 98, 23].
These synthetic samples are designed to possess similar statistical properties
as real, providing a valuable resource for research, development, and
evaluation in the field of biometrics. Synthetic image generators can be
utilized to generate more data with both inter and intra-class variations.
This helps overcome the limitations and challenges associated with
conventional iris datasets. Traditional iris datasets often suffer from
restricted sample sizes, lack diversity, and present concerns regarding
privacy and data sharing. In contrast, synthetically generated irides offer a
controlled and scalable solution that can mimic the complexity and diversity
of real-world iris traits [37, 23].

By generating synthetic irides, researchers and developers can gain access to
larger and more diverse datasets that facilitates comprehensive testing and
optimization of iris based biometric algorithms, enhancing their performance,
reliability, security and generalization capabilities. Additionally,
synthetically generated irides address privacy concerns associated with the
utilization of real biometric data. The artificial nature of the generated
data ensures that it is not directly linked to any specific individual,
mitigating the risks of unauthorized access or misuse of personal information.
Consequently, synthetic biometric datasets can be shared and distributed for
research and evaluation purposes without compromising individuals’ privacy
rights. Furthermore, synthetically generated irides play a crucial role in
enhancing the training and testing of deep convolutional neural network (CNN)
models. Deep CNNs have demonstrated remarkable performance in various
biometric tasks but heavily rely on large labeled datasets for effective
training. Synthetically generated irides can aid in augmenting the
availability of labeled training data by creating synthetic samples with known
ground truth annotations. This facilitates the creation of more extensive and
diverse training sets, resulting in improved CNN model training and higher
accuracy in iris recognition systems. Moreover, synthetically generated irides
contribute to the development of robust presentation attack detection (PAD)
algorithms. Presentation attacks (PAs), where an adversary attempts to deceive
a biometric system using fabricated or altered irides, pose significant
security risks. Various presentation attack scenarios can be simulated by
generating synthetic irides with diverse variations and attack types (for
example, cosmetic contact lens, printed eyes, artificial eyes, etc.). This
synthetically generated dataset enables the training and evaluation of PA
detection algorithms, enhancing their effectiveness and enabling the
development of resilient countermeasures against evolving PA techniques.

Thus, there are various applications of synthetically generated irides [37,
25, 29, 80, 89]:

  * •

Development and Testing: Synthetic iris datasets serve as a valuable resource
for algorithm development and testing in the field of biometrics. By
generating synthetic iris samples, researchers can evaluate the performance of
novel algorithms, compare different techniques, and benchmark their efficacy
against a standardized iris dataset. Synthetic data allows for controlled
experimentation, enabling researchers to precisely manipulate specific
biometric traits, variations, and noise levels to simulate different
operational scenarios. Therefore, by using synthetic data, researchers and
developers can assess system accuracy, robustness, and vulnerability to
various attacks or spoofing attempts. This evaluation process aids in
identifying system weaknesses, improving overall system performance, and
guiding the development of countermeasures.

  * •

Training Data Augmentation: Synthetic iris datasets can be utilized to augment
training datasets, enhancing the performance of iris recognition systems. By
generating additional synthetic samples, researchers can increase the size and
diversity of the training set, which helps to improve the generalization
capabilities of the algorithms. This approach reduces overfitting, enhances
the system’s ability to handle intra-class variations, and improves overall
recognition accuracy.

  * •

Privacy-Preserving: Synthetic iris dataset is invaluable for privacy-
preserving studies and research involving sensitive biometric information. It
allows researchers to conduct studies, simulations, and experiments without
the need for real individuals’ personal biometric data. Synthetic iris dataset
provides a privacy-friendly alternative that ensures data protection while
enabling advancements in biometric research and system development.

Therefore, synthetic iris datasets offer flexibility and convenience in the
development and assessment of iris based recognition systems and attack
detection methods. In this context, it is important to understand the
different methods employed to generate synthetic iris samples. In this paper,
we explore the current state-of-the-art synthetic iris image generation
methods and understand the strengths and weaknesses of these methods in terms
of the quality, identity uniqueness and utility of the generated images. 111We
consider images in the near-infrared (NIR) spectrum for this study. Thus, this
review brings forth specific contributions:

  * •

Study the current state-of-the-art methods to generate synthetic irides and
explain their pros and cons.

  * •

An assessment of synthetic iris images generated by current state-of-the-art
methods in terms of quality, uniqueness and utility.

  * •

Conduct experiments to analyze deep learning based iris recognition methods
and how synthetically generated iris datasets can enhance their performance.

  * •

Conduct experiments to analyze current deep learning based presentation attack
detection methods and how synthetically generated iris dataset can enhance
their performance.

  * •

Discuss the future direction to overcome the challenges of current methods to
generate enhanced synthetic iris datasets.

Figure 2: Examples of synthetically generated iris images using different
methods. (i) and (ii) use Texture-based non-deep learning methods from [95]
and [74]. On the other hand, (iii) and (iv) use GAN based methods from [87]
and [45], respectively. Former models tasked with producing synthetic
biometric samples strive to emulate the distribution of real human biometric
data, which inherently ties them to the variability and scope of the training
datasets. This can lead to challenges in replicating the intricate variations
found in real biometric traits, often yielding a synthetic dataset with a
narrow range of diversity. Additionally, certain models may fall short in
delivering high-quality and lifelike representations. Nonetheless, Generative
Adversarial Networks (GANs) have marked a significant advancement in crafting
synthetic images that closely resemble real ones. Yet, these images often bear
a strong similarity to the training data that comprises actual human images,
meaning that they lack distinctiveness in their identities. Moreover, some of
these techniques do not incorporate enough intra-class variation, failing to
capture the extensive spectrum of natural differences present in authentic
biometric data across real-world settings.  Figure 3: Some more examples of
synthetic irides that are generated using different Generative Adversarial
Networks (GANs) for partially and fully synthetic iris images. Table 1: Some
examples of iris datasets captured in different spectrum using various sensors
showcasing the lack of number of samples and also number of subjects in the
datasets.

Dataset Name | Devices | Spectrum | Number of Images (subjects)
---|---|---|---
CASIA-Iris-Thousand [4] | Iris scanner (Irisking IKEMB-100) | NIR | 20,000 (1000)
CASIA-Iris-Interval [4] | CASIA close-up iris camera | NIR | 2,639 (249)
CASIA-Iris-Lamp [4] | OKI IRISPASS-h | NIR | 16,212 (411)
CASIA-Iris-Twins [4] | OKI IRISPASS-h | NIR | 3,183 (200)
CASIA-Iris-Distance [4] | CASIA long-range iris camera | NIR | 2,567 (142)
ICE-2005 [1] | LG2200 | NIR | 2,953 (132)
ICE-2006 [2] | LG2200 | NIR | 59,558 (240)
IIITD-CLI [44] | Cogent and VistaFA2E single iris sensor | NIR | 6,570 (240)
URIRIS v1 [64] | Nikon E5700 | VIS | 1,877 (241)
URIRIS v2 [63] | Canon EOS 5D | VIS | 11,102 (261)
MILES [24] | MILES camera | VIS | 832 (50)
MICHE DB [92] | iPhone 5, Samsung Galaxy (IV + Tablet II) | VIS | 3,732 (184)
CSIP [72] | Xperia Arc S, iPhone 4, THL W200, Huawei Ideos X3 | VIS | 2,004 (100)
WVU BIOMDATA [15] | Irispass | NIR | 3,099 (244)
IIT-Delhi Iris Dataset [5] |  | JIRIS, JPC1000 and
---
digital CMOS camera
NIR | 1,120 (224)
CASIA-BTAS [92] | CASIA Module v2 | NIR | 4,500 (300)
IIITD Multi-spectral Periocular [75] | Cogent Iris Scanner | NIR, VIS, Night Vision | 1,240 (62)
CROSS-EYED [73] | Dual Spectrum Sensor | NIR, VIS | 11,520 (240)

##  2 Iris Recognition

The foundational technology behind modern automated iris recognition systems
can be traced back to the work of John Daugman [20], who is credited with the
development of the core algorithms that make such systems possible. Daugman’s
work leverages the distinct patterns found in the human iris for secure and
accurate human recognition. There have been significant improvements on his
initial work for various security, identification and privacy applications
[68, 50, 51]. Iris Recognition can be divided into 4 stages:

  * •

Iris Segmentation: Images in most iris datasets do not only contain the iris
itself but also region in the vicinity of the iris such as pupil, sclera,
eyelashes etc. So, the first step towards iris recognition is to segment the
iris from the captured image to remove unnecessary or extra information. Most
of the initial segmentation approaches, including Daugman [21], involve
identifying the pupil and iris boundaries. In traditional approaches, the
occlusions due to eye-lashed and eyelids are minimized by edge detection and
curve fitting techniques.

  * •

Normalization: Post segmentation, the variations in the size of segmented
irides (caused due to distance from sensor, or contraction and dilation of the
pupil) are minimized via geometric normalization where annular irides are
unwrapped to a fixed-size rectangular image.

  * •

Feature Encoding: Iris features are extracted from the geometrically
normalized iris image and encoded so that they can be used for matching. Most
common techniques for iris feature extraction involve Gabor Filtering [21],
BSIF [18], etc. that help in capturing the unique textural properties of the
iris.

  * •

Matching: Once features are encoded, various matching algorithms can be used
for iris recognition. Daugman [21] used Gabor phase information to encode the
iris and hamming distance to compare the encoded irides. These have been
improved over time to account for variations in image quality, occlusion and
noisy data.

For a detailed explanation of iris recognition, we recommend referring to [9],
[20], [59].

With recent developments in the field of deep-learning, deep networks have
found application in all 4 stages of iris recognition. Iris segmentation and
feature extraction have particularly benefited from deep learning-based
approaches as they often handle noise and complexities in iris datasets better
than traditional approaches. Nguyen et al. [59] studied the performance of
pre-trained convolutional neural networks (CNNs) in the domain of iris
recognition. The study reveals that features derived from off-the-shelf CNNs
can efficiently capture the complex characteristics of irides. These features
are adept at isolating distinguishing visual attributes, which leads to
encouraging outcomes in iris recognition performance. While the progress in
the field of deep learning has helped improve the reliability of iris
recognition systems by improving their performance under various conditions,
the lack of iris data with sufficient inter and intra-class variations limits
the training and testing of these systems. Therefore, we need to explore the
field of generative AI to generate synthetic iris datasets with sufficient
inter and intra-class variations to help train and test a robust iris
recognition system.

##  3 Iris Presentation Attack Detection

Presentation attack detection (PAD) is an essential component of iris
recognition systems. As the reliance on iris recognition systems grows, so
does the sophistication of attacks designed to exploit them. Here, we briefly
examine the nature of iris presentation attacks, the methodologies developed
to detect them, and the challenges faced in enhancing iris PAD systems.

Iris presentation attacks (PAs), sometimes known as spoofs, refers to physical
artifacts that aims to either impersonate someone or obfuscate one’s identity
or create a virtual identity (see Figure 4). Several types of presentation
attacks have been considered in the literature:

  * •

Print Attack: One of the simplest forms of iris PA involves the attacker
presenting a high-quality photograph of a valid subject’s iris to the
biometric system. Basic systems might be misled by the photograph’s visual
fidelity unless they are designed to detect the absence of depth or natural
eye movements.

  * •

Artificial Eyes: Attackers may employ high-grade artificial (prosthetic or
doll) eyes that replicate the iris’s texture and three-dimensionality. These
artificial eyes seek to deceive scanners that are not sophisticated enough to
discern liveness indicators such as the pupil’s response to light stimuli.

  * •

Cosmetic Contact Lens: A more nuanced approach includes the usage of cosmetic
contact lenses that have been artificially created with iris patterns that can
either conceal the attacker’s true iris or mimic someone else’s iris. This
type of attack attempts to bypass systems that match iris patterns by
introducing false textural elements.

  * •

Replay-Attack: Playing back a video recording of a bonafide iris to the sensor
constitutes another PA. Advanced iris recognition systems counter this by
looking for evidence of liveness, like blinking or involuntary pupil
contractions.

Figure 4: Some examples of real bonafide and PA iris images from MSU-Iris-PA01
[87]: (a) bonafide samples and (b) presentation attacks: (i) artificial eye,
(ii) & (iii) printed eye, (iv) Kindle display and (v) cosmetic contact lens.
[87, 88].

Researchers have proposed different methods to effectively detect different
types of PAs. In [31, 44] proposed to utilize textual descriptors like GIST,
LBP and HOG to detect printed eyes and cosmetic contact lens. Similarly,
Raghavendra and Busch [66] utilize cepstral features with binary statistical
image features (BSIF) to distinguish between bonafide irides and print
attacks. Another way to detect print attack is the liveness test that is
lacking in printed eyes [17, 39]. Liveness test can also be helpful to detect
attacks like artificial eyes. Eye gaze tracking [48] and multi-spectral
imaging [12] have good results in detecting printed eyes and artificial eyes.
[33, 54] proposed deep network based PA detection methods to detect different
types of PAs. To achieve this, Hoffman et al. [33] proposed a deep network
that utilizes patch information with a segmentation mask to learn features
that can distinguish bonafide from iris PAs.

While these iris PA detection methods perform well on various datasets,
attackers are continuously finding new ways to bypass them, leading to an arms
race between security experts and attackers. As a result, the PAD methods need
to be constantly updated (re-trained or fine-tuned) and tested against the
latest forms of attacks. This calls for PA detection methods that can
generalize well over new (or unseen) PAs without the hassle of re-training or
fine-tuning. Here, “Seen PAs” are those which the PAD methods have been
exposed to during the training phase. In contrast, “Unseen PAs” are not
included in the training phase, posing a concerning challenge for accurate PA
detection. Recent developments in PAD methods have focused on enhancing the
ability of systems to generalize, distinguishing bonafide irides from PAs,
even when encountering previously unseen PAs. Gupta et al. [30] proposed a
deep network called MVANet, which uses multiple convolutional layers for
generalized PA detection. This network not only improves PA detection
accuracy, but also addresses the high computational costs typically associated
with training deep neural networks by using a simplified base model structure.
Evaluations across different databases indicate MVANet’s proficiency in
generalizing to detect new and unseen PAs. In [76], Sharma and Ross proposed
D-NetPAD, a PAD method based on DenseNet to generalize over seen and unseen
PAs. It has demonstrated a strong ability to generalize across diverse PAs,
sensors, and data collections. Their rigorous testing confirms D-NetPAD’s
robustness in detecting generalized PAs [76].

Most of the PAD methods formulate PA detection as a binary-class problem,
which demands the availability of a large collection of both bonafide and PA
samples to train classifiers. However, obtaining a large number of PA samples
can be much more difficult than bonafide iris samples. Further, classifiers
are usually trained and tested across similar PAs, but PAs encountered in
operational systems can be diverse in nature and may not be available during
the training stage. Therefore, we need to explore the generative methods to
generate partially synthetic iris images (as identity is not the focus in PA
detection) that can help build a balanced iris PA datasets. This will help
researchers to better train and test their PAD methods.

##  4 Generating Synthetic Irides

As mentioned earlier, synthetic iris images offer several advantages,
including scalability, diversity, and control over the generated data. Some of
the methods to generate such images are listed below, categorized on the basis
of method used:

  * •

Texture Synthesis: This technique has been widely used for generating
synthetic iris images. These methods analyze the statistical properties of
real iris images and generate new images based on those statistics. Shah and
Ross [74] proposed an approach for generating digital renditions of iris
images using a two-step technique. In the first stage, they utilized a Markov
Random Field model to generate a background texture that accurately represents
the global appearance of the iris. In the subsequent stage, various iris
features, including radial and concentric furrows, collarette, and crypts, are
generated and seamlessly embedded within the texture field. In another
example, Makthal and Ross [52] introduced an approach for synthetic iris
generation using Markov Random Field (MRF) modeling. The proposed method
offers a deterministic synthesis procedure, which eliminates the need for
sampling a probability distribution and simplifies computational complexity.
Additionally, the study highlights the distinctiveness of iris textures
compared to other non-stochastic textural patterns. Through clustering
experiments, it is demonstrated that the synthetic irides generated using this
technique exhibit content similarity to real iris images. In a different
approach, Wei et. al. [83] proposed a framework for synthesizing large and
realistic iris datasets by utilizing iris patches as fundamental elements to
capture the visual primitives of iris texture. Through patch-based sampling,
an iris prototype is created, serving as the foundation for generating a set
of pseudo irises with intra-class variations. Qualitative and quantitative
analyses demonstrate that the synthetic datasets generated by this framework
are well-suited for evaluating iris recognition systems.

  * •

Morphable Models: Morphable models have been utilized for generating synthetic
iris images by capturing the shape and appearance variations in a statistical
model. These models represent the shape and texture of irises using a low-
dimensional parameter space. By manipulating the parameters, synthetic iris
images with different characteristics, such as size, shape, and texture, can
be generated. Most of the research in this category focuses on generation
synthetic iris images with gaze estimation and rendering eye movements. Wood
et. al. [84] proposed a 3-D morphable model for the eye region with gaze
estimation and re-targeting gaze using a single reference image. Similarly,
[7] focuses on achieving photo-realistic rendering of eye movements in 3D
facial animation. The model is built upon 3D scans of a face captured from
various gaze directions, enabling the capture of realistic motion of the
eyeball, eyelid deformation, and the surrounding skin. To represent these
deformations, a 3D morphable model is employed.

  * •

Image Warping: Image warping techniques involve applying geometric
transformations to real iris images to generate synthetic images. These
transformations can include rotations, translations, scaling, and
deformations. Image warping allows for the generation of synthetic iris images
with variations in pose, gaze direction, and occlusions. In [11], Cardoso et.
al. aimed to generate synthetic degraded iris images for evaluation purposes.
The method utilizes various degradation factors such as blur, noise,
occlusion, and contrast changes to simulate realistic and challenging iris
image conditions. The degradation factors are carefully controlled to achieve
a realistic representation of degraded iris images commonly encountered in
real-world scenarios. In [16], a method combining principal component analysis
(PCA) and super-resolution techniques is proposed. The study begins by
introducing the iris recognition algorithm based on PCA, followed by the
presentation of the iris image synthesis method. The proposed synthesis method
involves the construction of coarse iris images using predetermined
coefficients. Subsequently, super-resolution techniques are applied to enhance
the quality of the synthesized iris images. By manipulating the coefficients,
it becomes possible to generate a wide range of iris images belonging to
specific classes.

  * •

Generative Adversarial Networks (GANs): GANs have gained significant attention
for generating realistic and diverse synthetic iris images. In a GAN
framework, a generator network learns to generate synthetic iris images, while
a discriminator network distinguishes between real and synthetic images. The
two networks are trained in an adversarial manner, resulting in improved image
quality over time. GANs can generate iris images with realistic features,
including iris texture, color, and overall appearance. Minaee and Abdolrashidi
[55] proposed a framework that utilizes a generative adversarial network (GAN)
to generate synthetic iris images sampled from a learned prior distribution.
The framework is applied to two widely used iris datasets, and the generated
images demonstrate a high level of realism, closely resembling the
distribution of images within the original datasets. Similarly, Kohli et. al.
[45] proposed iDCGAN (iris Deep Convolutional Generative Adversarial Network),
a novel framework that leverages deep convolutional generative adversarial
networks and iris quality metrics to generate synthetic iris images that
closely resemble real iris images. Bamoriya et. al. [6] proposed an novel
approach, called Deep Synthetic Biometric GAN (DSB-GAN), for generating
realistic synthetic biometrics that can serve as large training datasets for
deep learning networks, enhancing their robustness against adversarial
attacks.

Currently, GAN-based methods to generate synthetic biometrics have been proven
to be far superior to capture the intricate details of various biometric cues.
Therefore, in the remaining of the paper we will mainly focus on these methods
and iris images generated by these methods for our study and analysis.

###  4.1 Partially Synthetic Irides

Partially-Synthetic data refer to synthetic samples that contain artificial
components mixed with real biometric data. In this approach, certain aspects
or attributes of the biometric data are synthetically generated, while other
parts are derived from real individuals (see Figure 5). The goal of partially-
synthetic data is to introduce controlled variations or augmentations to the
real data, thereby increasing the diversity and robustness of the dataset.
This can be particularly useful in scenarios where the real data is limited,
imbalanced, or lacks specific variations. For example, in iris presentation
attack (PA) detection where the detection methods aim to detect PA attacks
(such as printed eyes, cosmetic contact lens, etc.), limited PA data is
available to train the detection methods. This can limit the methods’
development and testing as well. Also, with the improvement in technology more
advance PA attacks are present in the real world (such as good quality
textured contact lens, replay attack using high definition screens, etc.) and
the current detection methods are not generalized enough to detect these new
and unseen attacks. As mentioned earlier, Kohli et al. [45] proposed iDCGAN
that utilizes a deep convolutional generative adversarial network to generate
synthetic iris images that are realistic looking and closely resemble real
iris images. This framework aims to explore the impact of the synthetically
generated iris images when used as presentation attacks on iris recognition
systems. In [6], Bamoriya et al. proposed a novel approach, DSB-GAN, which is
built upon combination of convolutional autoencoder (CAE) and DCGAN and the
evaluation of DSB-GAN is conducted on three biometric modalities: fingerprint,
iris, and palmprint. One of the notable advantages of DSB-GAN is its
efficiency due to a low number of trainable parameters compared to existing
state-of-the-art methods. Yadav et al. [87, 88] leverages RaSGAN to generate
high-quality partially synthetic iris images in NIR spectrum and evaluates the
effectiveness and usefulness of these images as both bonafide and presentation
attack. They also proposed a novel one-class presentation attack detection
method known as RD-PAD for unseen presentation attack detection, addressing
the challenge of generalizability in PAD algorithms. Zou et al. [97] proposed
4DCycle-GAN that is designed to enhance the database of iris PA images by
generating synthetic iris images with cosmetic contact lenses. Building upon
the Cycle-GAN framework, the 4DCycle-GAN algorithm stands out by adding two
discriminators to the existing model to increase the diversity of the
generated images. These additional discriminators are engineered to favor the
images from the generators rather than those from real-life captures. This
approach reduces the bias towards generating repetitive textures of contact
lenses, which typically make up a significant portion of the training data. In
[89], Yadav and Ross proposed to generate bonafide as well as different types
of presentation attacks in NIR spectrum using a novel image translative GAN,
known as CIT-GAN. The proposed architecture translates the style of one domain
to another using a styling network to generate realistic, high resolution iris
images.

Figure 5: Some examples of partially-synthetic iris PAs (a: Printed eyes, b:
artificial eyes and c: cosmetic contact lens) generated using CIT-GAN [90].

###  4.2 Fully Synthetic Irides

Fully-synthetic biometric data refer to entirely artificial biometric samples
that do not correspond to any real individuals in the training data. These
synthetic samples are created using mathematical models, statistical
distributions, or generative algorithms to simulate the characteristics of
real biometric data (see Figure 6). Some of the texture based methods focuses
on generating new iris identities (fully-synthetic) that are unique from
training samples. These methods aim to generate new iris images with both
inter and intra-class variations to help mitigate the issue of small training
data size by increasing the size of the dataset. This can help in the
development of recognition systems and their testing. Also, by generating
fully-synthetic identities that do not correspond with real identities, we can
solve the privacy concerns associated with using a real person’s biometric
data. Wang et al. [81] proposed a novel algorithm for generating diverse iris
images, enhancing both the variety and the number of images available for
analysis. The technique employs contrastive learning to separate features tied
to identity (like iris texture and eye orientation) from those that change
with conditions (such as pupil size and iris exposure). This separation allows
for precise identity representation in synthetic images. The algorithm
uniquely processes iris topology and texture through a dual-channel input
system, enabling the generation of varied iris images that retain specific
texture details. Yadav and Ross [90] proposed iWarpGAN that aims to
disentangle identity and stylistic elements within iris images. It achieves
this through two distinct pathways: one that transforms identity features from
real irides to create new iris identities, and another that captures style
from a reference image to infuse it into the output. By merging these modified
identity and style elements, iWarpGAN can produce iris images with a wide
range of inter and intra-class variations. Limited work has been done in this
category to generate irides with identity that do not match with any identity
in the training data. This is an important and upcoming topic that needs more
focus.

Figure 6: Some examples of fully-synthetic irides generated using iWarpGAN
with inter and intra-class variations [90]. SScore here refers to simiarity
score between two iris images using VeriEye.

##  5 Generating Adversarial Networks (GANs)

A generative adversarial network (GAN) consists of two networks: a generator
and a discriminator, which are trained simultaneously through adversarial
learning. The generator network aims to generate data, such as images, audio,
or text, that is indistinguishable from real data, while the discriminator
aims to differentiate between real and generated data. In this study, we have
utilized five different types of GANs that has been shown in the literature to
generate realistic looking iris images:

  * •

Relativistic Average GAN (RaSGAN): RaSGAN [36] aims to overcome the
shortcomings of traditional GANs by introducing a relativistic discriminator.
In standard GANs, the discriminator’s objective is to distinguish between real
and fake samples, while the generator’s objective is to generate samples that
are indistinguishable from real ones. However, in RaSGAN, both the generator
and discriminator are trained to consider the relative likelihood that a real
sample is more realistic than a synthetic one and vice-versa. The aim is to
provide the discriminator with feedback not only on from the real and
synthetic samples but also on how realistic each sample is with respect to
each other. Thus, improving the stability and convergence properties of GAN by
providing more informative gradients to both the generator and discriminator.
RaSGANs have been shown to produce realistic synthetic images across various
domains, including images [36, 87], text [58], and audio [93, 57].

  * •

StarGAN-v2: Choi et al. [14] proposed a multi-domain image translative GAN
knowns as StarGAN-v2 that aims to generate realistic looking images in
multiple domains. The domains here refers to styles such as hair color, facial
expression, etc. The generator in StarGAN-v2 takes a source image and a
reference style code generated by mapping network as inputs and translate the
source image into a synthetic image that is from domain exhibiting style
properties same as reference style code. This method showed improvement for
multi-domain synthetic image generation when compared with StyleGAN [42] and
StarGAN [13].

  * •

Cyclic Image Translative GAN (CIT-GAN): Yadav and Ross [88] proposed CIT-GAN
to generate different types of iris PAs as well as bona-fide images. In this
method, the generator aims to translate given input image into an image from
reference domain. This translation is facilitated by a Style Network that
takes reference image as input and actively learns to generate style code that
clearly indicates the style properties of reference domain. In comparison to
StarGAN-v2 that uses mapping network to generate style code from a random
noise vector, this method is able to learn the intra-class variations that are
present in the multiple domains. Therefore, improving the diversity in the
generated images for each domain. The ”cyclic” term here refers to cycle-
consistency loss that is utilized to ensure that the translated image can be
translated back to the original source domain. This helps ensure that while
translating the image from source domain to reference, underlying
characteristic of the input image such as iris-pupil shape and size are not
changed.

  * •

StyleGAN-3: Karras et al. first proposed StyleGAN [42] for good quality
synthetic image generation with some improvements in StyleGAN-2 [40] to handle
multi-domain image generation, but these approaches lack in terms of diversity
in images being generated and generalization capability of the network. In
[41], Karras et al. proposed StyleGAN-3 that overcome these shortcomings by
introducing an adaptive discriminator augmentation (ADA), which aims at
improving the generalization capability of the discriminator network. ADA
dynamically adjusts the strength of data augmentation during discriminator
training, effectively making the discriminator more robust to diverse
variations in the training data.

  * •

iWarpGAN: In [90], Yadav and Ross proposed iWarpGAN that aims to generate
fully synthetic iris images i.e., the identity in the iris images generated by
this method doesn’t resemble any identity seen during training. This is
achieved by disentangling identity and style using two transformation
pathways: (1) Style transformation pathway: aims to generate iris images with
styles that are extracted from a reference image, without altering the
identity (2) Identity transformation pathway: aims to transform identity of
input image in the latent space to generate identities that is different from
input image. This method can generate iris images with identities unique from
training data, generate multiple images per newly generated identity and
scalable to hundred-thousand identities.

Figure 7: Yadav and Ross [88] proposed CIT-GAN where the generator aims to
translate given input image into an image from reference domain. This
translation is facilitated by a Style Network that takes reference image as
input and actively learns to generate style code that clearly indicates the
style properties of reference domain Figure 8: Yadav and Ross [90] proposed
iWarpGAN that aims to generate fully synthetic iris images i.e., the identity
in the iris images generated by this method doesn’t resemble any identity seen
during training. This is achieved by disentangling identity and style using
two transformation pathways: (1) Style transformation pathway: aims to
generate iris images with styles that are extracted from a reference image,
without altering the identity (2) Identity transformation pathway: aims to
transform identity of input image in the latent space to generate identities
that is different from input image.

##  6 Comparative Analysis

In this section, we discuss the different experiments that were conducted to
evaluate the capability of different GAN methods for generating fully and
partially synthetic iris images. For this, we test the realism of generated
images with respect to real iris data using different methods. Further,
uniqueness of the generated images in terms of identity and utility of the
generated images for PA detection and iris recognition is evaluated in this
section. The different GAN methods we studied in this research are: RaSGAN
[36, 87], CIT-GAN [88], StarGAN-v2, StyleGAN-3 [96] and iWarpGAN [90].

###  6.1 Datasets Used

In this paper, we conducted our experiments and analysis using three iris
datasets:

  * •

CASIA-Iris-Thousand [4]: Developed by the Chinese Academy of Sciences
Institute of Automation, the CASIA-Iris-Thousand dataset is a popular resource
for studying iris patterns and for advancing iris recognition technologies.
This dataset comprises 20,000 iris images from 1,000 participants, accounting
for 2,000 distinct identities when considering images of both the left and
right eyes. These images are captured at a resolution of 640x480 pixels. The
dataset has been partitioned into training and testing subsets, with a
distribution of 70% for training (1,400 identities) and 30% for testing (600
identities).

  * •

CASIA Cross Sensor Iris Dataset (CSIR) [86]: The training portion of the
CASIA-CSIR dataset, provided by the Chinese Academy of Sciences Institute of
Automation, was employed in our study. It includes a total of 7,964 iris
images from 100 individuals, representing 200 unique identities when both eyes
are considered. Similar to the first dataset, a 70-30 split based on unique
identities was used to divide the images into training (5,411 images) and
testing sets (2,553 images), intended for the training and evaluation of deep
learning models for iris recognition.

  * •

IITD [5]: Originating from the Indian Institute of Technology, Delhi, the
IITD-iris dataset was collected in an indoor setting and consists of 1,120
iris images from 224 subjects. The images were captured using JIRIS JPC1000
and digital CMOS cameras, each with a resolution of 320x240 pixels. In line
with the previous datasets, this one also utilizes a 70-30 split based on
unique identities for its training (314 identities) and testing sets (134
identities).

For the preparation of the data, we processed and resized all the iris images
to 256x256 pixels, centering on the iris region as determined by the iris and
pupil coordinates from VeriEye.222www.neurotechnology.com/verieye.html

####  6.1.1 Iris PA Datasets

Our exploration into synthetic images for iris PA detection involves
leveraging five distinct iris PA datasets. These datasets included Casia-iris-
fake [77], Berc-iris-fake [49], NDCLD15 [22], LivDet2017 [91], and MSU-
IrisPA-01 [87], each comprising authentic iris images alongside various
categories of PAs such as cosmetic contacts, printed iris images, artificial
eyes, and display-based attacks. As mentioned earlier, we processed and
resized the images to 256x256 pixels, centering on the iris region as
determined by the iris and pupil coordinates from VeriEye. Images that VeriEye
failed to process correctly were excluded from the study, with our focus being
primarily on the aspect of image synthesis. The resulting processed dataset
for analysis contains 24,409 genuine iris images, 6,824 images with cosmetic
contact lenses, 680 artificial eye representations, and 13,293 printed iris
images. The train and test division on this dataset is explained later in this
section.

###  6.2 Quality of Generated Images

In order to evaluate the realism and quality of the generated iris images,
different GAN methods- RaSGAN, CIT-GAN, StarGAN-v2, StyleGAN-3 and iWarpGAN-
are trained using real irides from CASIA-Iris-Thousand, CASIA Cross Sensor
Iris and IITD-iris dataset, separately. Using the trained networks, we
generate three sets of 20,000 synthetic bonafide images (from each dataset)
for each of the GANs mentioned above. We then evaluate the realism of the
generated images and the quality of the iris using three different methods:
(1) Fréchet Inception Score [71], (2) VeriEye Rejection Rate and (3) ISO-
ISO/IEC 29794-6 Standard Quality Metrics [3].

(a) FID score distribution from generated iris images when GANs are trained
using CASIA-Iris-Thousand dataset. (b) FID score distribution from generated
iris images when GANs are trained using CASIA-CSIR dataset.

Figure 9: Histograms showing the realism scores of real iris images (i.e., FID
scores) from three different datasets and the synthetically generated iris
images (continued). Lower the FID score, the more realistic the generated iris
images are. (a) FID score distribution from generated iris images when GANs
are trained using IITD-iris dataset. Figure 10: Histograms showing the realism
scores of real iris images (i.e., FID scores) from three different datasets
and the synthetically generated iris images (continued). Lower the FID score,
the more realistic the generated iris images are.

####  6.2.1 Fréchet Inception Distance Score

The Fréchet Inception Distance (FID) Score is a metric used to assess the
quality of synthetically generated images by comparing their distribution to
that of real images, resulting in a score based on the differences. The
objective is to minimize this score, as a lower FID score suggests greater
resemblance between the synthetic and real datasets. It has been noted that
FID scores can span a broad range, with extremely high scores in the 400-600
range indicating significant deviation from the real data distribution and,
consequently, poor synthetic image quality [71].

In our specific analysis of the quality of synthetically generated iris images
produced by different GANs used in this study, we obtained an average FID
score of 24.33 and for RaSGAN and StarGAN-v2. On the other hand, a score of
31.82, 26.90, 15.72 and 17.62 were obtained for CIT-GAN, StyleGAN-3 and
iWarpGAN, respectively. As mentioned earlier, lower the FID score, more
realistic the generated images are with respect to real images. Therefore, we
conclude that StyleGAN-3 and iWarpGAN generate the most realistic iris images.
The distribution of these FID scores has been shown in Figure 10.

(a) CASIA-Iris-Thousand dataset versus synthetic iris images from various
GANs. (b) CASIA-CSIR dataset versus synthetic iris images from various GANs.

Figure 11: Histograms depicting the quality of real irides alongside the
quality of synthetic irides. These evaluations are in accordance with the
ISO/IEC 29794-6 Standard Quality Metrics, with the quality scale set between 0
and 100, where a higher score denotes superior quality. Iris images that were
not successfully assessed by this standard were assigned a score of 255. As
seen from the figure, score distribution for real images are closely resembled
by images generated using iWarpGAN, StyleGAN-3, followed by CIT-GAN. However,
same can not be said for RaSGAN and StarGAN-v3. (a) IITD-iris dataset versus
synthetic iris images from various GANs. Figure 12: Histograms depicting the
quality of real irides alongside the quality of synthetic irides. These
evaluations are in accordance with the ISO/IEC 29794-6 Standard Quality
Metrics, with the quality scale set between 0 and 100, where a higher score
denotes superior quality. Iris images that were not successfully assessed by
this standard were assigned a score of 255. As seen from the figure, score
distribution for real images are closely resembled by images generated using
iWarpGAN, StyleGAN-3, followed by CIT-GAN. However, same can not be said for
RaSGAN and StarGAN-v3.

####  6.2.2 VeriEye Rejection Rate

For this experiment, we followed the protocol mentioned in [90] to evaluate
the effectiveness of the various generators by computing the rejection
333Proportion of images that could not be processed. rate of the images when
analyzed by VeriEye, a commercial iris matcher. In the first set of
comparisons using the IITD-Iris-Dataset, which comprises 1,120 real iris
images, only 0.18% were rejected by VeriEye. In contrast, 1,120 synthetic
images produced by RaSGAN and StarGAN-v2 had a rejection rate of 4.55% and
4.64%, respectively. However, images generated by CIT-GAN, iWarpGAN and
StyleGAN-3 demonstrated significantly lower rejection rates at 2.85%, 0.73%
and 1.07%, respectively.

The CASIA-CS Iris dataset contains 7,964 real iris images with a rejection
rate of 2.81%. The rejection rates for 7,964 synthetic images were notably
higher; images generated by RaSGAN and StarGAN-v2 were rejected at a rate of
2.06% and 2.65%, respectively. Meanwhile, CIT-GAN, iWarpGAN and StyleGAN-3
produced images with rejection rates closer to the real images, at 2.71%,
2.74% and 2.52% respectively.

Lastly, the CASIA-Iris-Thousand Dataset, which included 20,000 real iris
images, saw a very low rejection rate of 0.06%. Synthetic images from this
dataset indicated that RaSGAN and StarGAN-v2 had the highest rejection rate at
0.34% and 0.27%, respectively. Synthetic images from CIT-GAN, iWarpGAN and
StyleGAN-3 showed improvement with rejection rates of 0.24%, 0.18% and 0.16%,
respectively.

####  6.2.3 ISO/IEC 29794-6 Standard Quality Metrics

As described in [90], the fidelity of synthetically produced iris images is
assessed using the ISO/IEC 29794-6 Standard Quality Metrics [3]. This
assessment was applied to images generated by different GANs utilized in this
study. The ISO standard employs a set of criteria to evaluate the quality of
an iris image, which includes the usable iris area, the contrast between the
iris and sclera, image sharpness, the contrast between the iris and pupil,
pupil shape, and more, culminating in a comprehensive quality score. This
score is on a scale from 0 to 100, where 0 indicates the lowest image quality
and 100 the highest. Images that fail to be evaluated by this ISO metric,
typically due to substandard quality or errors in segmentation, are assigned a
score of 255.

As shown in Figure 12, the quality scores for 20,000 synthetic iris images
obtained using iWarpGAN, CIT-GAN and StyleGAN-3 are on par with those of real
iris images. Conversely, a noticeable number of images generated by RaSGAN
were assigned the score of 255, reflecting their inferior quality.
Additionally, a comparison across the three datasets showed that the CASIA-
CSIR dataset contained a higher proportion of images with the lowest score of
255, in contrast to the IITD-iris and CASIA-Iris-Thousand datasets.

###  6.3 Uniqueness of Synthetically Generated Irides

This experiment examines the uniqueness of the iris images generated
synthetically using different GANs, specifically assessing the ability of
these methods to create distinct identities that exhibit some intra-class
variations. For this, RaSGAN, CIT-GAN, StarGAN-v2, StyleGAN-3 and iWarpGAN-
are trained using real irides from the train set of CASIA-Iris-Thousand, CASIA
Cross Sensor Iris and IITD-iris dataset, separately.

Unique-Experiment-1: This experiment is centered on exploring the uniqueness
of the synthetically generated iris datasets, which were generated using
various GAN techniques, with respect to the training examples. To achieve
this, the analysis involve comparing the impostor and genuine distribution for
real irides that were part of the training set with that of the synthetically
generated irides. Here, VeriEye matcher is utilized to obtain the similarity
score between two iris images, with the score spanning from 0 to 1557, where a
higher score indicates a more accurate match.

Unique-Experiment-2: This experiment aims to investigate the uniqueness and
intra-class variability present in the synthetically generated iris dataset.
This involves an analysis of both genuine and imposter distributions within
the generated dataset and their comparison with the distributions from actual
iris datasets. As previously noted, this investigation is conducted across a
range of uniquely generated identities to assess their uniqueness. The VeriEye
matcher is used in this experiment for assessing the similarity score between
pairs of iris images.

Analysis: The above-mentioned experiments replicate the experimental protocols
defined in [90] to evaluate the inter and intra-class variations in the
generated iris dataset. It also analyzes the distinction of the generated
identities from the real irides in the training dataset. As depicted in
Figures 13, 14, and 15, the iris images produced by iWarpGAN do not exhibit a
high degree of resemblance to the real irides from training set, unlike the
outcomes from other GAN methods. This indicates iWarpGAN’s proficiency in
generating iris patterns with identities that diverge from those found in the
training set. Moreover, by examining the overlap between the imposter
distribution of the synthetically generated iris images and that of the
genuine iris images, it becomes evident that the generated identities are
distinct from one another. Hence, we can conclude that iWarpGAN has the
capability to generate fully synthetic irides images with identities that are
significantly different from irides in training set, while other GANs are
efficient in generating only partially synthetic irides.

Table 2: PAD-Experiment-0: True Detection Rate (TDR in %) at three different
False Detection Rate (FDR) of different iris PAD methods in baseline
experiment (PAD-Experiment-0) where PA detectors are trained using real
bonafide and PA samples. The PA samples used in this experiment are imbalanced
across different PA classes [89].

|  BSIF+SVM [22] |  Fine-Tuned VGG-16 [27] |  Fine-Tuned AlexNet [47] |  D-NetPAD [76]
---|---|---|---|---
TDR (@0.1%) | 3.32 | 85.25 | 86.10 | 87.94
TDR (@0.2%) | 6.15 | 83.86 | 87.29 | 88.91
TDR (@1.0%) | 28.11 | 89.07 | 90.51 | 92.54

Table 3: PAD-Experiment-1: True Detection Rate (TDR in %) at 1% False
Detection Rate (FDR) of different iris PAD methods when trained using real
bonafide irides, real PAs and synthetic PAs generated using different GAN
methods. Comparing the baseline performance with the performance here, we can
see that iris PA images generated by StyleGAN-3 and iWarpGAN are better at
replacing the real PAs.

|  BSIF+SVM [22] |  Fine-Tuned VGG-16 [27] |  Fine-Tuned AlexNet [47] |  D-NetPAD [76]
---|---|---|---|---
RaSGAN | 28.31 | 81.11 | 86.74 | 87.97
CIT-GAN | 29.43 | 85.81 | 88.37 | 88.86
StarGAN-v2 | 29.73 | 83.47 | 88.71 | 88.45
StyleGAN-3 | 34.05 | 88.14 | 90.95 | 90.75
iWarpGAN | 32.09 | 86.58 | 89.18 | 90.28

Table 4: PAD-Experiment-2: True Detection Rate (TDR in %) at 1% False
Detection Rate (FDR) of different iris PAD methods when they are trained using
real bonafide irides alongside a balanced collection of PA samples using both
real and synthetic irides. The performance here shows the utility of generated
irides in improving the performance iris PAD methods.

|  BSIF+SVM [22] |  Fine-Tuned VGG-16 [27] |  Fine-Tuned AlexNet [47] |  D-NetPAD [76]
---|---|---|---|---
RaSGAN | 50.52 | 88.62 | 88.15 | 94.90
CIT-GAN | 51.11 | 91.60 | 92.70 | 97.89
StarGAN-v2 | 50.69 | 92.39 | 88.38 | 95.59
StyleGAN-3 | 56.14 | 95.02 | 93.38 | 98.39
iWarpGAN | 55.25 | 93.69 | 92.99 | 98.20

Figure 13: This figure shows the uniqueness of iris images generated using
iWarpGAN when the GANs are trained using CASIA-Iris-Thousand dataset. The
y-axis represents the similarity scores obtained using VeriEye. Here, R=Real,
S=Synthetic, Gen=Genuine and Imp=Impostor. As indicated by the arrow here,
similarity between images generated by iWarpGAN is the lowest in comparison to
other GANs, followed by StyleGAN-3. This indicates the uniqueness of the
identity generated by these GANs with respect to real identities and also the
generated identities. Figure 14: This figure shows the uniqueness of iris
images generated using iWarpGAN when the GANs are trained using CASIA-CS iris
dataset. The y-axis represents the similarity scores obtained using VeriEye.
Here, R=Real, S=Synthetic, Gen=Genuine and Imp=Impostor. As indicated by the
arrow here, similarity between images generated by iWarpGAN is the lowest in
comparison to other GANs, followed by StyleGAN-3. This indicates the
uniqueness of the identity generated by these GANs with respect to real
identities and also the generated identities. Figure 15: This figure shows the
uniqueness of iris images generated using iWarpGAN when the GANs are trained
using IITD iris dataset. The y-axis represents the similarity scores obtained
using VeriEye. Here, R=Real, S=Synthetic, Gen=Genuine and Imp=Impostor. As
indicated by the arrow here, similarity between images generated by iWarpGAN
is the lowest in comparison to other GANs, followed by StyleGAN-3. This
indicates the uniqueness of the identity generated by these GANs with respect
to real identities and also the generated identities.

###  6.4 Utility of Synthetically Generated Irides

The experiments in this section evaluate the usefulness of the synthetically
generated irides in the task of presentation attack detection as well as iris
recognition.

####  6.4.1 Presentation Attack Detection

As discussed earlier, lack of sufficient number and variations of PA samples
can affect the generalizability of different PA detection methods, especially
those based on deep networks that need large number of training samples for
better performance. Therefore, we outline experimental protocol that was
established to assess the utility of the synthetically generated iris PAs. To
generate the synthetic data (for both bonafide and PAs), the GANs are trained
using 14,970 bonafide iris images, 6,016 printed eyes, 4,014 cosmetic contact
lenses and 276 artificial eyes from the iris PA datasets mentioned earlier.
Note that CIT-GAN, StarGAN-v2, StyleGAN-3 and iWarpGAN can handle style
transfer from one domain to another, i.e., image generation using these GANs
can extend to multiple domains/style. However, RaSGAN can facilitate only
single domain image generation. Therefore, multiple RaSGAN networks are
trained to be able to generate different types of PAs as well as bonafides.

We analyzed the efficacy of several iris Presentation Attack Detection (PAD)
techniques, including VGG-16 [27], BSIF [22], D-NetPAD [76], and AlexNet [47],
within these different experimental configurations for analytical scrutiny. It
is noteworthy that D-NetPAD has been recognized as one of the top-performing
PAD algorithms, particularly highlighted for its performance in the iris
liveness detection challenge (LivDet-20 edition).

PAD-Experiment-0: In this baseline experiment, we demonstrate the performance
of different iris PAD methods on iris PA datasets mentioned in Dataset
section. The PAD methods are trained with 14,970 bonafide iris images and
10,306 instances of PAs, which include 4,014 cosmetic contact lenses, 276
artificial eyes and 6,016 printed eyes. For testing, the dataset contain 9,439
bonafide iris images alongside 9,896 PA instances, which broke down into 2,720
cosmetic contacts, 404 artificial eyes and 6,772 printed eyes.

PAD-Experiment-1: Here, we aim to evaluate the realism of synthetically
generated PAs against real PAs by training the iris PA detection methods with
14,970 bonafide samples and synthetically generated 6,016 printed eyes, 276
artificial eyes and 4,014 cosmetic contact lenses. For testing, the set
contains 9,439 bonafide iris images alongside 9,896 PA instances, that
consists of 2,720 cosmetic contacts, 404 artificial eyes and 6,772 printed
eyes.

PAD-Experiment-2: Here, we aim to evaluate the usefulness of the generated PA
sampled for balanced training when imbalanced PA classes are improved by
adding synthetically generated PAs to the training. Therefore, in this
experiment, PAD methods are trained using 14,970 bonafide irides alongside a
balanced collection of 15,000 PA samples. This assembly comprises of 276
artificial eyes, 4,014 cosmetic contact lenses, and 5,000 printed eyes which
are real PAs; in addition, 4,724 synthetic artificial eyes and 986 synthetic
cosmetic contact lenses are utilized in the training set. Similar to the
previous experiment, testing is done on 9,439 bonafide iris images alongside
9,896 PA instances that consists of 2,720 cosmetic contacts, 404 artificial
eyes and 6,772 printed eyes.

Analysis: The variation in the number of samples across different PA
categories influences the effectiveness of the PAD techniques. This impact is
evident when the outcomes of PAD-Experiment-0 are compared with PAD-
Experiment-1 & 2\. In PAD-Experiment-2, the PAD methods are trained with 9,439
bonafide samples and an equalized set of PA samples (namely, 5,000 from each
PA category), including both real and synthesized PAs. Based on the data in
Table 2 and Table 4, there is a discernible enhancement in the performance of
each PAD approach when trained with balanced samples from each class.
Moreover, a comparative analysis of synthetic PA samples and actual PA samples
was conducted through PAD-Experiment-1. In this experiment, a portion of the
real PA samples in the training set was substituted with synthetic PAs. When
comparing the performance metrics in Table 2 and Table 3, only a marginal
difference in PAD efficacy is noticeable.

####  6.4.2 Iris Recognition

As mentioned earlier, the lack of sufficient number of unique identities in
the dataset with large intra-class variations can affect the training and
testing of many iris recognition methods, especially recognition methods based
on deep networks that needs large number of training samples for better
performance. As seen from the previous experiments, among the GANs studied in
this paper, only iWarpGAN has the capability of generating irides whose
identities are sufficeintly different from that of the training data.
Therefore, we train iWarpGAN using the CASIA-Iris-Thousand, CASIA-CSIR and
IIT-Delhi iris datasets, separately, to generate synthetic irides with both
inter and intra-class variations. The generated dataset is utilized in this
experiment to evaluate its usefulness for improved iris recognition.

Recog-Experiment-0: In this baseline experiment, EfficientNet [34], ResNet-101
[56] and DenseNet-201 are trained using the triplet training approach.
Training and testing has been done using cross-dataset method, i.e., when
trained using real irides from CASIA-Iris-Thousand and CASIA-CSIR, testing is
done on IITD-iris dataset.

Recog-Experiment-1: This experiment focuses on assessing the impact of
synthetic iris dataset on enhancing the performance of iris recognition
methods based on deep learning. In this context, EfficientNet, ResNet-101 and
DenseNet-201 are trained with not only the real irides from CASIA-Iris-
Thousand, CASIA-CSIR, and IITD-iris datasets but also with a synthetically
generated iris dataset derived from iWarpGAN.

Analysis: Figures 16 and 17 illustrate that the recognition accuracy of iris,
matchers based on deep learning, is enhanced with the incorporation of a
larger dataset with synthetic samples. This enhancement is particularly
evident when the matcher is trained using a combination of both real iris
images and those synthetically produced by iWarpGAN. Although the baseline
performance of ResNet-101 and EfficientNet is somewhat modest, they exhibits a
notably substantial enhancement in the performance. Similar, behavior is seen
for DenseNet-201.

##  7 Summary & Future Work

In this section, we summarize the studies conducted in this paper and also
discuss the future scope in the field of generating synthetic irides.

###  7.1 Current Techniques & their Limitations

In this research, we reviewed and analyzed different GAN methods to generate
synthetic images for both bonafide irides and different presentation attack
instruments. The generated irides were evaluated for their realism, quality,
unique identities and utility. Using these experiments as our criteria for
comparison, we conclude that: (1) GAN methods like RaSGAN, StarGAN-v2 and CIT-
GAN can generate realistic looking synthetic dataset, but fail to generate
enough samples whose identities are different from those in the training
dataset, i.e., the identities in the generated dataset has high similarity
with the training dataset and also with itself. Similar behavior was seen for
StyleGAN-3, however the images generated by StyleGAN-3 are highly realistic
and very close to original dataset in terms or quality (as seen in Figure 12).
On the other hand, iWarpGAN showed the capability of generating synthetic
irides that has both inter and intra-class variations, which can augment real
iris datasets for training and testing iris recognition methods. This method
is scalable to multiple domains (using attribute vector), and can also be
utilized to generate bonafide and various PAs that can be used to enhance the
performance of different PAD methods (as shown in 4). While this method
provides solutions for both fully and partially synthetic iris generation,
iWarpGAN utilizes image transformation, whereby the network requires both an
input and a style reference image to modify the identity and style, resulting
in the generation of an output image. Such a process could potentially
constrain the range of features that iWarpGAN is able to explore. Also, there
is still some similarity observed between training samples and generated
irides.

Figure 16: This figure shows the performance of DenseNet-201, EfficientNet and
ResNet-101 in the cross-dataset evaluation scenario i.e., trained using CASIA-
Iris-Thousand & CASIA-CSIR datasets and tested using IIT-Delhi iris dataset.
Improvement in the performance is seen when size of training set is increased
using synthetic irides. Figure 17: This figure shows the performance of
DenseNet-201, EfficientNet and ResNet-101 in the cross-dataset evaluation
scenario i.e., trained using CASIA-Iris-Thousand & IIT-Delhi iris datasets and
tested using CASIA-CSIR dataset. Improvement in the performance is seen when
size of training set is increased using synthetic irides.

###  7.2 Future Work & Scope

Numerous researchers are dedicating their efforts to the creation of synthetic
face images encompassing varied attributes, styles, identities, spectra, and
more [61, 53, 65, 46]. However, more study has to be done in the field of
synthetic iris generation. This opens up a wide array of opportunities that
warrant in-depth investigation and exploration. Some of the possible future
work in this field are listed here:

  * •

Generalizable Solution for Fully-Synthetic Iris Images: The first area of
exploration involves developing a generalizable solution for creating fully
synthetic iris images. This involves not just replicating the physical
appearance of an iris but also ensuring that the synthetic images can adapt or
respond to different lighting conditions and camera specifications, just as a
real iris would. Such a solution would have huge implications for enhancing
the realism and applicability of synthetic irides in various fields, including
iris recognition and presentation attack detection. Also, demographic
attributes such as gender, age, etc. needs to be accounted for while
generating synthetic iris images.

  * •

Generating Ocular Images: Another intriguing direction is the generation of
complete ocular images, which include not only the iris but also other parts
of the eye. So far, the research work in this field manly focuses on
generating cropped iris images and for some cases the image quality
deteriorates as more information is introduced in the image [88]. Therefore,
this area needs attention from researchers to be able to study the other
distinguishing features of an eye apart from irides [11, 43]. Creating
realistic ocular images that accurately represent the myriad variations in
human eyes could also aid in the development of more robust facial recognition
technologies by providing a method to generate faces that also captures the
intricate details of a real iris, which seems to be missing from most of the
face generation methods.

  * •

Synthetic Iris Videos to Mimic Liveness of Real Irides: The creation of
synthetic iris videos that can mimic the liveness of real irides is a
particularly challenging topic. Such advancements would be beneficial in
developing more robust PA detection methods. By simulating the natural
movements and minute dynamic changes in the iris, these videos could provide
an authentic and effective tool for training and improving liveness detection
algorithms in iris recognition systems. Also as mentioned earlier, this could
also aid in developing a robust facial recognition technologies.

  * •

Multi-spectrum Iris Image Generation: The generation of multi-spectrum iris
images presents another frontier [70, 10]. The human iris exhibits different
characteristics under various light spectrum - a feature that is often
leveraged in biometric systems. Developing synthetic iris images that can
accurately reflect these multi-spectral properties would not only enhance the
realism of these images but also expand their utility in biometric recognition
systems. Such multi-spectrum images could serve as a valuable resource for
researchers and developers, offering a versatile tool for testing and
improving multi-spectral iris recognition technologies.

  * •

Improved GAN Latent Space Interpretability: As indicated by iWarpGAN,
enhancing GANs to produce disentangled representations in their latent space
is vital for generating full synthetic iris images. Here, improving semantic
interpretability can help ensure that changes in the latent space correspond
to meaningful and coherent changes in the generated output [38, 8]. Also,
developing new visualization techniques to map and understand the high-
dimensional latent space is another crucial step.

  * •

Diffusion Diffusion Generative Adversarial Networks: Diffusion GANs [82] offer
a promising approach for generating synthetic iris images with high realism
with respect to real iris images. By employing a diffusion process, these
networks iteratively refine generated images over multiple steps, allowing for
better control and coherence in synthesis. With this, Diffusion GANs can
capture complex spatial dependencies inherent in iris textures, ensuring the
production of realistic patterns consistent with real iris images. Moreover,
they exhibit improved stability and convergence during training, mitigating
issues like mode collapse and artifact generation.

  * •

Application in Deepfake detection: Synthetic images can play a crucial role in
deepfake detection [67, 94] by serving as a tool for training and evaluating
detection algorithms. By incorporating a diverse range of synthetic data into
the training process, detection algorithms can better generalize and identify
subtle inconsistencies or artifacts indicative of deep fakes. Moreover,
synthetic images allow researchers to experiment with various image editing
techniques, enhancing the robustness of detection systems against emerging
threats. Therefore, synthetic images can serve as a critical resource in
advancement and development of robust deep fake detection methods.

The potential applications of successfully generated synthetic iris images are
vast and varied. In security and biometric recognition systems, these images
can help improve the accuracy and robustness of systems by providing a diverse
range of data for training and testing. In the medical field, synthetic iris
images could be used for training purposes, enabling medical professionals to
recognize and diagnose eye-related diseases more effectively.

Furthermore, in the realm of entertainment and virtual reality, realistic
synthetic iris images could enhance the visual experience by providing more
lifelike and expressive characters. The ability to generate eyes that
accurately mimic human emotions could revolutionize the way we interact with
virtual environments and characters. In conclusion, while the generation of
realistic and unique synthetic iris images is still in the development stage,
it presents an opportunity for research and exploration.

##  8 Acknowledgments

This research is based upon work supported by by NSF CITeR funding. The views
and conclusions contained herein are those of the authors and should not be
interpreted as necessarily representing the official policies, either
expressed or implied by NSF CITeR.

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