# Learning scale-variant features for robust
iris authentication with
deep learning based ensemble framework

Siming Zheng1*, Rahmita Wirza O.K. Rahmat2‡, Fatimah Khalid3‡, Nurul Amelina
Nasharuddin4‡ 1Computer Assisted Surgery and Diagnostic (CASD), Department of
Multimedia, Putra Malaysia University, 43400 UPM Serdang, Malaysia
2C1,Department of Multimedia, Putra Malaysia University, 43400 UPM Serdang,
Malaysia
3C2,Department of Multimedia, Putra Malaysia University, 43400 UPM Serdang,
Malaysia
4C2,Department of Multimedia, Putra Malaysia University, 43400 UPM Serdang,
Malaysia

###### Abstract

In recent years, mobile Internet has accelerated the proliferation of smart
mobile development. The mobile payment, mobile security and privacy protection
have become the focus of widespread attention. Iris recognition evolves into a
high-security authentication technology in these fields, and widely used in
distinct science fields in biometric authentication researches. The
Convolutional Neural Network (CNN) is one of the conventional deep learning
approach for image recognition, whereas its anti-noise ability is weak and
needs a certain amount of memory to train in image classification tasks. Under
these conditions we improved the architecture of Mask R-CNN and put forward
the fine-tuning neural network architectures based on mobile Inception V4,
which integrate every component in an overall system that combines the iris
detection, extraction, and recognition function as an iris recognition system.
The proposed framework has the characteristics of scalability and high
availability; it not only can learn the scale-variant features by the zero-
padding normalization but also enhancing the robustness of the whole learning
framework. Importantly, our custom architectures can be trained by using
different spectrum of samples, such as Visible Wavelength (VW) and Near
Infrared (NIR) iris biometric image data. The recognition average accuracy of
99.10% is achieved while executing in the mobile edge calculation device of
the Nvidia Jetson Nano.

###### keywords:

Robust Iris Authentication System , Scale-variant Features , Zero-padding
Normalization , Ensemble Learning , Fine-tuning , GPU-Based Edge Devices ,
TensorBoard.

††journal: ArXiv.org.

These authors contributed equally to this work.
‡These authors also contributed equally to this work.
*Corresponding author: Siming Zheng1*
*E-mail<EMAIL_ADDRESS>
*The first version was at arXiv:1912.00756v1, [v1] Mon, 2 Dec 2019
*This is the second revision, [v2] Sat, 13 June 2020

##  1 Introduction

The essential character of iris informatization is the digitalization and
recessiveness. On the one hand, the iris is one of the complex organs of the
human body, the hidden password in the eyeball is richer, and contains much
more random texture patterns than using the Personal Identification Number
(PIN), fingerprint, and the human face [2] [3]. Under these favorable
conditions, iris characteristics can provide its value for identifying
encryption technology. On the other hand, more and more iris recognition
technologies have been applied to mobile devices, such as smart mobile phones,
tablets, and human-machine interactive devices, due to the GPU with high-
performance graphics processing capabilities [4] [5] [6].

Over the last two decades, smart mobile devices have been embedded with built-
in high-resolution imaging sensors. The sensor can be used to perform iris
recognition tasks and allow researchers to explore appropriate solutions to
finish all the detailed stages of iris recognition in a mobile environment.
Some iris authentication functions are supported in earlier mobile devices.
For example, the first smartphone with an iris authentication in the world
called Arrowsnxf-04G [7], which is equipped with an infrared camera and a
light-emitting diode (LED), the camera can scan and decode the images of user
iris. To support the iris authentication development, Samsung S8 series mobile
phones added Infrared Radiation (IR) and iris camera in its front lens, using
the front camera assists the iris camera with infrared LED to determine the
approximate general outline of the user. The iris camera scans the iris
information through the light source and then converts the iris information
into a specific code. Finally, the system compares the code with a known
password to determine to unlock or not. Huawei company introduced GPU Turbo
technology in 2019, which can greatly energize the performance of the graphics
processing on several smartphones P30 Pro series [8], up to 60%. In addition,
Nvidia Shield tablet K1 [9] equipped Kepler architecture with 192 cores
streaming graphics multiprocessors, which supports thousands of threads to
implement high-performance calculations in parallel.

Although the processing power of these mobile devices is growing, the system
robustness is still the most concerned about. Currently, there are two
research problems have to be addressed in a complex mobile environment [10]
[11]. In this research, the first problem is how to process and recognize the
high quality images of iris with scale-variant features, because most of the
mobile phones are equipped with a high-definition camera and always places in
an uncooperative environment. The second problem relates to how to improve the
recognition accuracy on target iris images under the different spectrum for
applying in different practical scenarios scenarios. Therefore, our research
objective is to investigate the structure of the multi-learning model to solve
the research problems above in mobile environment.

The remainder of this paper is organized as follows: We briefly introduce
related works about iris authentication in Section II. Section III puts
forward the proposed iris authentication framework with multiple critical
components for iris region extraction and matching. Section IV shows
experimental findings in favor of using the proposed method and the analysis
in Section V. Finally, we discuss and conclude the research in Section VI and
VII.

##  2 Related Works

Iris authentication is a process of identifying individuals based on the iris
shape and texture distribution. We have reviewed the strengths and weaknesses
of current iris authentication studies. As a result of the survey, several
studies have shown that some scholars used similarity computation methods for
measuring the similarity between the two iris templates, as posited in [12]
[13] [14] [15] [16] and got a lot of valuable conclusions.

The most critical work of the iris verification system is to detect the iris
and outer boundary correctly. In the study [17] of Deshpande et al., they used
Daugman’s integral and differential algorithm [18] in their experiment. Once
the iris boundary is detected, the program converts the iris to a standard
size and encodes it in the iris template for matching between the iris
templates. The Rubber Sheet model is used for the normalization of the iris
image. In the final matching phase, applying a 1D logarithmic Gabor filter for
the iris feature extraction, while the Hamming distance is used as a matching
algorithm to compare two biometric templates for iris verification. One of the
challenges in seeking the boundaries is the iris images with low contrast or
low lighting during the detection, Deshpande’s algorithms overcome some
difficulties in the non-uniform illumination as well as the reflections. His
works enhance the performance of the segmentation and normalization process in
iris authentication systems and achieve an overall accuracy of 95% with robust
characteristics.

In Mohammed Hamzah Abed’s work [19], he adopted Circular Hough transform to
detect an iris in the recognition system with a precision rate of 98.73% on
average. And then the Haar wavelet transforms extracted the raw features from
iris images for fast computing and low storage. The method of Principal
Component Analysis (PCA) was to alleviate a variety of noises for image
reduction and improved discriminative information for matching. In the verify
phase, the method of Cosine distance measured the similarity between two non-
zero vectors of inner product space, the result of the experiment shows that
the method is effective with a classification accuracy of 91.14%. Subsequent
works have continued to use the same experimental data as a way to explore the
performance of the proposed framework. Kaudki [20] introduced a new thought of
iris preprocessing, integrated the method of Rubber-Sheet Unwrapping and Haar
wavelet transforms into every experimental object. Rubber-Sheet Unwrapping
Normalization has the ability to deal with different sizes of extracted images
due to the changes in pupil size caused by external lighting. The processing
of normalization mainly improves the clarity of acquired features.
Furthermore, the Haar wavelet transform is applied for feature extraction
because of its computational simplicity. The Hamming distance, as a measure of
the characteristic distance, is used to conduct and validate the target data.
The recognition accuracy does not continue to increase substantially, and it
remains at around 97%. By analysis, recognizing the high-quality iris images
is the weakness of the traditional template matching method. Iris images
processed in a series of transformations suffer from significant degradation
[21], making iris recognition between the training set and testing set with
less relevance. It might be due to the image degradation by some image fusion
techniques [22] [23] [24], and feature extraction operation like Hough
transform [25] [26] [27]. These findings reinforce the importance of
researching the influence of image quality.

To measure the robustness, Bansal proposed a fuzzy filtering-based approach in
the iris recognition system [28]. It consists of fuzzy noise detection, noise
filtration, normalization, data enhancement, and feature extraction
operations. All of sub-modules have been proposed to clean the inputted iris
image before they are subjected to iris recognition. The merit of the proposed
approach is high recognition rate of 99.75%. However, the call chain is too
complicated in the program that it causes some problems like module design
complexity (iv(G)) in a mobile environment [29]. The high iv(G) means that the
module has a high degree of coupling, making the module difficult to isolate,
maintain, and reuse. Importantly, such complex computation process leads to an
increment in the CPU utilization, which translates into throughput gains in
the mobile terminal. How to improve the module structure has become a big
problem for researchers to be faced with [30] [31].

In a comprehensive study of iris recognition, Chiara Galdi et al. [32] found
that the combination of different classifiers describing various factors of
the iris can get an ideal accuracy in iris recognition. In the color
descriptor, the comparison of Euclidean distance is computed between the two
images based on the color histograms, and the texture descriptor calculated a
number of features by using the Minkowski Bouligand dimension [33] [34]
through a process of decomposition. Furthermore, the cluster descriptor
provided important information, such as centroid coordinates, orientation, and
eccentricity. When comparing two iris images, the algorithm matches the list
of two cluster feature vectors according to the all-versus-all scheme and
calculates the average distance of each matching pair. All descriptors are
implemented via a weighted sum for recognizing the iris. Finally, the best
matching with a distance value is obtained from each matching pair, and the
final weight value can predict and determine whether the iris images observed
belong to the same category or not. The studies show that there are still
existing problems like low recognition rate in the images with different
resolutions due to insufficient feature acquisition by the classifiers [35]
[36] [37].

As was mentioned in the previous reviews, researchers must focus on not only
the performance for processing and recognizing high-resolution iris data but
also the storage space for operation data in mobile environments. Moreover,
the systems of iris authentication on mobile devices should take a short time
to decide whether a person is real or an impostor.

##  3 Material and methods

A robust iris authentication system includes detection, segmentation,
normalization, and recognition submodule [38]. Every submodule is sequentially
performed in the Graphics Processing Unit (GPU) in our experiments. Fig 1
below shows the flow of pre-processing required for the training model. We
added two kinds of layers, extraction and normalization, as the robust
components, which are implemented respectively in our proposed model. By doing
this, the normalized feature is better for the iris recognition phase.

Fig 1. The flow of pre-processing. The flowchart shows every step that need to
be performed before iris recognition model (From left to right). Notice that
we propose the custom sub-components of extraction and normalization, which
are both within the scope of the research for extracting and improving the
scale-variant iris features in our learning model architecture. See the boxes
with dotted border area.

###  3.1 The Improved Mask R-CNN Architecture

The preprocessing of iris automatic detection is a crucial stage in the iris
authentication system. We analyzed that some potential factors may be
susceptible to interference, which could affect recognition performance [39].
For instance, the natural iris texture can be easily obscured by cosmetic
contacts, and this has a significant impact on the segmentation of the iris
area. The experimental dataset consists of thousands of iris images taken from
different angles and under various conditions are affected by a variety of
internal and external factors.

Fig 2. The challenges of UTiris datasets in a different spectrum. The color
iris images (A and B) are acquired in the VW and acquired grey iris images (C
and D) under the NIR. The images were taken from [45].

As can be seen from example A in Fig 2, a slender specular highlight is shown
in the iris area near the nose. Thus, the performance is reduced owing to the
light reflection or uneven illumination in the area of iris. Example B showing
a permeable contact lens covered in the iris region, this can generate some
large artifacts in the detection region. Example C of iris image showing a
small portion of eyelashes that make a considerable influence on structural
iris texture. Example D of iris image exhibiting non-ideal iris region due to
poor coordination of human-machine interaction, this causes many iris features
to be lost. The challenges we described in the detection and extraction
phases, from wearing the contact lens to the eyelashes occlusion, to specular
highlights of illumination, to uncooperative action. Besides, the shape of the
eyelids varies from one individual to another. All of these factors make the
localization of the eyelids more difficult [40] [41] [42].

To detect iris area at a fine-grained level, we custom the Mask R-CNN [43]
neural network architecture. The model of Mask R-CNN is an extended structure
of Faster R-CNN [44], including a function of pixel-to-pixel alignment. The
proposed Mask R-CNN is constructed by stacking the layer for predicting the
iris location in each Region of Interest (ROI), called a mask branch, this
layer is similar to the existing boundary layer and classification layer. Our
Mask R-CNN architecture mainly performs two different operations: object
detection and semantic segmentation, which are used for the instance
segmentation base on iris datasets, and their aims to extract and normalize
the iris region from the object images.

####  3.1.1 The RPN Component

Region Proposal Networks (RPN) is an essential component of Mask R-CNN. It is
a lightweight neural network that used to replace the selective search in the
model of Faster-RCNN. Similar to Faster R-CNN, the purpose of RPN is to seek
and generate the region proposals effectively. Besides, RPN supports
calculations on the GPU to improve processing speed and back-propagation by
different sizes.

Fig 3. The generated anchor points distributed on the raw iris image. The
figure above visualizes the effect of anchor distribution. The number of
anchors is obtained through the RPN network, which is used to select positive
and negative samples and finally calculate the difference between the positive
sample and the ground truths [43].

In the RPN architecture, a sliding window is used for scanning the image and
finding the area where the target of the iris exists. It is a rectangle
distributed over the image area, as demonstrated. The center of every sliding
window is the anchor as shown in Fig 3, and every anchor is implicated in the
aspect ratio and the scale [43] [44]. The sliding windows are implemented by
the RPN convolution, and they scan the surrounding area based on the anchor
points at high speed. Besides, sliding windows scan all areas in parallel mode
by using the GPU and complete the operation within 10ms.

By default, we used three aspect ratios and three scales, resulting in 9
anchors at each sliding position. For example, for the size of W×H
convolutional feature map, the 3x3 convolution networks slide on W×H by the
padding operation and the stride is 1, meaning that the sliding window has W×H
slides, and each slide has 9 anchors in each scanning operation. Finally, the
number of W×H×9 anchors are generated, the role of RPN is to use these anchors
to determine the location of the feature map and the size of its bounding box.

At each sliding window position, multiple scanned regions are considered as
candidates, which are predicted simultaneously. In our setting, a limited
number of the highest potential candidate regions for each iris image is
counted as k boxes. In the region layer of Mask-RCNN, the outputs of 4k
parameters are needed to encode the coordinates of 4 different points for the
k boxes, and the classification layer requires 2k values to evaluate the
probability that each region is the target or not.

Fig 4.The processing of iris region extraction. The processing of iris region
extraction. The figure demonstrates how the iris localization and iris region
extraction, which are performed to provide discrimination information into a
neural network in detail. The scale and aspect ratio of the anchor is
controlled by the “SCALES” and “RATIOS” parameters in configuration.

The Fig 4 above illustrates the processing to seek and exact the ROI of iris.
Using the sliding window and anchors, we obtained W×H×9 proposals from one
original iris image. Next, each proposal generated six parameters: two
parameters (0 and 1) are used to label the foreground and the background
probability, which is to calculate the target by comparing each proposal and
ground truth; Meanwhile, each proposal is transformed into ground truth size
by the translation and contraction operation. In this processing, it needs
four parameters (upper-left, upper-right, lower left and lower right) for the
locating four coordinates since the differences in the position and size of
each proposal and ground truth. Once six parameters are appropriately set, all
the scale-variant features of the detected region can be output, including the
various coordinates of iris detection. Besides, this enables us to implement
further normalization function by using the extracted iris image with the
highest probability.

####  3.1.2 The Instance Segmentation on Experimental Data

The experimental iris images not only contain the regions of interest (ROI) of
iris but also existing redundant identifying information, such as eyelashes
and sclera, etc. Thus, the current raw iris images cannot be used directly in
the training model. We focused on iris preprocessing and recognition
implements in the robustness and practicability of the system. The crucial
ability of our framework is to extract unique properties from the iris images,
and this makes it easier to create a specific code of an iris for everyone. In
our experiment, the reconstructed Mask R-CNN is used for locating and
extracting the iris with scale-variant features. Before inputting unique
scale-variant iris features and texture into the recognition model, two steps
need to be performed in our robust iris authentication system, including the
iris extraction and normalization.

In the iris region detection, we propose efficient detection method
specifically for the iris region, limbic and boundary of the eyelid on our
dataset. To collect some training-testing samples, we create some ground truth
in the dataset of UTiris for building iris ground truth. The experimental data
is a set of color and grey iris images with high-resolution 2048×1360 or
1000×776 pixels. To begin with, we randomly select 158 iris images from UTiris
dataset [45], and each user has two images for training the Mask R-CNN model.
The generated image is labeled as 1 if there is evidence of the iris in the
sliding window of 299×299 pixel or 0 background, as presented in Fig 5.

Fig 5. The annotation on the fine-grained iris images.Corresponding pairs of
iris ground truth acquired in the VW and NIR session. The figure given at the
left side is original iris dataset and the right side is iris ground truth
layer with red region.

Then, to enhance the robustness of the model input, we generated a multi-
channel space that duplicates the same array from grey space to a
representative grey image. The new arrays formed by stacking the given arrays
in three dimensions. We adopt the pre-trained model train on the COCO dataset
[46]. By matching the testing set and ground truths, the proposed Mask-RCNN
calculates their matched probability to output the best one, which are
intended for referencing position to locate the iris region of the raw iris
images, as indicated in Fig 6(B). Ground truths were matched across different
iris examples can be seen in (DOI 10.6084/m9.figshare.10280492). If the raw
iris image was successfully matched by the top probability of iris ground
truth, which can generate four coordinates. They correspond to four corners of
a square in Fig 6(C). The iris position can be routed through the squares with
different sizes, the results of iris localization displayed in Fig 6(D), the
experimental results show good localization performance (DOI
10.6084/m9.figshare.10280501). After the iris is located, the iris outer
boundary may contain some periocular textures, such as sclera, eyelid and
pupil, which can contribute to further analysis and correlation [47]. In
addition, all of these textures also play a significant role that is expected
to complement iris as auxiliary features for improving the recognition effect
in non-cooperative environments [48] [49]. The same calculation mode is
applied for the grey iris images arraying from Fig 6(E) to 6(H).

Fig 6. The iris localization with iris ground truths. To achieve iris region
localization, three normalized steps corresponding to the top iris ground
truth (2nd column), iris region (3rd column), iris periocular (4th column),
are obtained under the different spectrum images applied by the function of
proposed Mask-RCNN instance segmentation. The upper row in each sub-diagram
shows successful iris region segmentation in the VW session, and the bottom
row shows the segmentation in the NIR session.

###  3.2 The Zero-padding Normalization on the Scale-variant Features

During the testing recognition model, the fully connected layer needs to
reshape the pooled results after the convolutional layers, so the input images
of the fully connected layer need to be a set of fixed size images. If the
dimension of the input vector is not fixed, then the number of the weight
parameter of the full connection is variable, which results in slow testing
and dynamic changes of the network, and has almost no effect on parameter
training. In the previous step, all of extracted iris region images are the
same wide size of 299 pixels, but all images are diverse in height size (less
than 299 pixels). To fill these vacant pixels is an effective way for
enhancing and training the recognition model.

Fig 7. The optimized Mask R-CNN architectures in the mobile environment. The
first dotted box illustrates that we implement two input modes are applied
during the preprocessing of the system, VW(Color) and NIR(Grey) modes. The
second dotted box illustrates that the iris features map with discriminative
information was obtained by our custom extraction layer and these scale-
variant features are processed by the custom zero-padding layers. Each
individual component proposed in our Mask R-CNN architecture does influence
iris recognition performance.

There are two essential components in the TensorFlow, which are the
convolution layer and the pooling layer. The convolution output sizes obtained
by the two modes are always different [50]. We can choose the “Valid” and
“Same” modes to handle the padding mode of the output data. The purpose of
padding component is to determine whether to fill the additional edge pixels
of the input image matrix when performing convolution or pooling operations.
Based on this concept, in order to enable the images processed by the proposed
Mask R-CNN to be trained in the further recognition model, we have customized
a zero-padding layer in the architecture of Mask R-CNN to make the shapes
match of the output image as needed, and directly input into the further
recognition model, as shown in Fig 7.

Fig 8. The proposed zero-padding normalization on extracted iris images with
different sizes. The experimental results show that the iris region is
extracted from the raw data (left, original iris image), then the extracted
scale-variant iris features (middle, iris region) is transferred into a
standard training set(right, processed iris image with padding layer).

Most importantly, our proposed framework can efficiently enhance the
robustness of the model output. It can extract the iris region by the
extraction layer, and the custom padding layer can fill the vacant area with
pixels in the vertical direction and the width kept the same. All the value of
filled pixels is 0 with black color [50] [51]. The top row represents the
color iris image, while the bottom row represents the gray iris image.
Finally, all iris images are cropped to the 299×299 pixels feature maps, as
displayed in Fig 8. The advantage is that it can reduce GPU computation during
the training model, and the stability of the recognition model for image
processing is enhanced.

###  3.3 The Fine-Tuned Mobile Inception V4 Architecture

The fine-tuned mobile Inception V4 is executed to identify the human iris. For
our experimental dataset, the iris image represents a specific domain, such as
iris periocular and iris characters, which belongs to the human special
central visual system. For these particular texture images, a strategic
priority for us is that we would fine-tune the Inception V4 neural networks
continue training it on the iris dataset we have.

Many novel models and efficient learning techniques have been introduced to
make CNN’s model deeper and powerful [52] [53], achieving revolutionary
performance in a wide range of inputted data. Szegedy et al. [54] proposed an
improved mobile version of Inception v4 based on Inception v3 [55]. Inception
v4 primarily consists of an input stem, three different Inceptions and two
Reduction modules. Based on this structure, we have developed a fine-tuned
model based on Inception v4. The proposed model includes a final classifier
layer, and its dimensions differ from the original model. Fig 9 shows the
complete architecture of the proposed model and the part of the fine-tuning
with the dashed box. The implement of iris recognition on a mobile device is
significantly different from the environment on a dedicated device, the former
relies on the computational power and the limited space for storage.

Fig 9. The fine-tuned Inception V4 architecture. The final pooling procedure
is described as a series of adaptive pooling combinations, where the filter is
with different output sizes. The final full connection layer is adjusted
according to the number of classifications in this experiment. These two
subcomponents (with two dotted boxes above) are the optimized structures
generated by our fine-tuning method.

Below are the implementation datils of our fine-tuned Inception V4 neural
network model:

First of all, we fine-tuned the first layer of our model based on the original
Inception V4 model. The input size is adjusted to 299×299 pixels. Different
networks require different input sizes. For example, our fine-tuned Inception
V4 requires that the image size of the input network is 299×299 pixels.
Therefore, before the iris image is input to the proposed network, images need
to be downsampled in the specified size for network input.

To make the model recognize the iris more efficiently in a mobile environment,
we loaded the pre-training weights and custom last classification layers to
develop our model. Our task is to make the network focus on learning scale-
variant features in the final linear layer. Siyu Huang et al. propose simple
but effective variants of the pooling module, named stacked pooling [56]. The
stacked pooling is an equivalent form of multi-kernel pooling [57] by stacking
smaller kernel pooling. All the pooling operations are calculated on down-
sampled feature maps except for its first kernel pooling, which can reduce the
computing cost for training the learning model. Their empirical studies reveal
that the stacked pooling shows a better computing efficiency than multi-kernel
pooling. In our fine-tuned model, we stacked three adaptive average pooling
layers, the first two layers are two-dimensional, and the output sizes are 3×3
and 2×2, respectively. The last one output size is 1 that applies a 1D
adaptive average pooling over an input signal composed of several input
planes.

In addition, our iris recognition is a classification task on the UTiris
datasets, the new fully connected layer of the proposed network will be of 79
categories instead of the original 1000 categories. The adjustment is to
reconstruct the fully connected layer (the softmax layer), which is
implemented by a linear layer combining with the identity operator.

A good selection of activation function is an important part of the design in
a neural network. In the process of backpropagation, the gradient has
direction and size. The gradient descent algorithm multiplies a variable
called the learning rate to determine the location of the next point. A paper
[58] by Leslie N. Smith describes some very instructive learning rate settings
to find the initial learning rate. If the learning rate was low, the gradient
was decline slowly, and the training took a long time. By contrast, if the
learning rate setting was large, then it was difficult to converge to the
extreme value. Considering the experiment in practice, we use the value of
1e-4 as the learning rate in our proposed model.

Finally, we need to use an optimization algorithm to iterate over the model
parameters to minimize the loss function value. In many fields such as
computer vision, the most commonly used is the gradient descent method to find
the optimal loss during the training phase [59]. Although the Adam algorithm
is currently the mainstream optimization algorithm, some researchers also
pointed out the defect of Adam’s convergence [60] [61]. Compared with Adam,
the AMSGrad gradient descent function is more robust to the parameter changes
and is more stable in the training process [62]. Thus, AMSGrad can substitute
for the regular Adam without the momentum parameter in our proposed model.

Our proposed model is to adjust and add the learning layers to optimize the
Inception V4 and investigate whether they can improve decision making in the
recognition process in the mobile environment. The methodologies above details
the implementation of the multi-task deep learning architectures for single
iris object detection on our edge computing device.

###  3.4 The Iris Datasets

To evaluate the performance of the proposed framework, we selected one well-
known iris dataset of UTiris, which includes the iris image acquisition scheme
by using different devices [45]. The database consists of a total of 1540 iris
images, including the Visible-Wavelength (VW) and the Near-Infrared (NIR)
session. All the iris images are captured under non-constraint conditions,
such as non-ideal images, imaging distance, and illumination conditions. There
are 804 images of 2048×1360 pixels in the VW session, and others 736 images of
1000×776 pixels in the NIR session, are taken from 79 individuals demonstrated
in 158 classifications. The dataset is collected by the University of Tehran
during 24-27th of June 2007.

###  3.5 The K-fold Cross Validation

The cross-validation [63] emphasizes the objective evaluation of the matching
degree of the model to the data. This data analysis method [64] divides the
original data into k groups, makes a verification for each subset and takes
the rest of the k-1 subset data as the training set, thus the cross-validation
can train K models. Particularly, the K models are evaluated in the validation
set, and the final loss is obtained by the weighted mean operation via the
loss of each model. The loss function of our recognition model is implemented
by the cross-entropy loss function, combining the log softmax and negative log
likelihood functions to calculate the model loss. This strategy is capable of
measuring subtle differences and is suitable for applying in fine-grained
image classification tasks.

###  3.6 The Edge Calculation Devices

The integrated framework above is executed on a GPU-based edge calculation
device - Jetson Nano. The Jetson Nano is proposed by Nvidia [65], which is
intended for low-power applications requiring high computational performance
in mobile environments. It equipped with Maxwell GPU with Quad ARM Cortex-A57
processor, and 4GB of LPDDR4 memory. Learning models can run with Linux kernel
3.10.96 on the Ubuntu 18.04 system. Design of low-power consumption (5 watts)
and integrated GPU makes the Jetson Nano become an ideal candidate for
conducting the proposed methods in this research. Due to its strong computing
capabilities [66], researchers can perform multiple neural networks by
parallel mode in certain fields, such as image classification, segmentation,
and object detection in the complex mobile environment.

##  4 Results

###  4.1 The Evaluation of Proposed Mask R-CNN Architecture

In our experiment, we randomly use some portion of the iris image as the
training set and then use the Callback function to save the best weights and
load them before predicting the new iris images. There are approximately 20%
data for training and 80% for testing the proposed Mask R-CNN. When finished
the preprocessing phase, all iris images were fixed to 299×299 pixel to by the
normalization operation in order to meet the model input size, this can
efficiently reduce the capacity of the training set and increase the training
speed for the further training model, as shown in Fig 10.

Fig 10.Different degrees of padding processing effects. Comparing the
different degrees of image processing effects with the color (Fig 10A-10D) and
gray iris images (Fig 10E-10H). The left-to-right images represent the padding
effect sorted the iris by size, from smallest to largest in padding area.

The experiment results in Fig 10 show that the effect of experimental images,
which testified the effectiveness and practicality of the proposed Mask R-CNN
architecture. Different noise factors can be distinguished from the Fig 10
such as multiple scales, color distortion, and insufficient light. Among the
most important is that some eyes are open while the other eye half-open or
closed, which affects the performance and stability in the biometric
identification process. Adding the zero-padding layer in the architecture is
the implementation of the solution to our research problems, which can
efficiently learn the scale-variant features from each iris training set and
enhance the robustness of the iris authentication system. The proposed Mask
R-CNN also uses a multi-task loss function to calculate the loss, combining
the loss of classification, localization and segmentation mask. The equation
is defined as below.

$\displaystyle Loss$ $\displaystyle=L_{cls}+L_{box}+L_{mask}$

The formula consists of Loss functions in each ROI region: classification loss
and position regression loss of the bounding box. They were inherited from
Faster R-CNN and the Loss of the mask is proposed by [43]. During training the
model, the Loss evidently dropped to nearly 0.01% and to the lowest of 0.0066%
at 203rd and 233rd of 250 epochs, respectively. Our proposed Mask R-CNN
architecture correctly identified 1539 of 1540 iris images in UTiris datasets,
the fail one is due to the improper capture of partial iris images. Finally,
the precision of iris detection is more than 99.99%. This section summarizes
our Mask R-CNN architecture, including the stronger robustness of detecting
the iris location and the extracting the precise region of the iris region,
are presented. A baseline dataset has been completed that serves as a basis to
measure evaluation targets. Next, we comprehensively validate the performance
of the proposed framework by using a pre-processed iris dataset.

###  4.2 The Evaluation of Fine-tuned Mobile Inception V4 Architecture

In the recognition phase, our framework was conducted on UTiris datasets and
analyze the performance of the fine-tuned mobile Inception V4. The total size
of the training set is 1540 images, which were randomly sorted in the memory.
The learning rate of the whole network was initialized to 1e-4. The testing
iterations was set to 17 epochs on the mini-batches of eight images during the
training, using the AMSGrad optimization algorithm. All of these super-
parameters were set properly so that the network be able to generalize well.
Simultaneously, these factors could be used to improve the performance and
accuracy of our iris authentication systems.

Fig 11. The 5-fold cross validation

The cross-validation strategy was employed to verify the performance. As
illustrated in Fig 11, we applied Five fold cross validation [67] to evaluate
the performance of our proposed mobile Inception V4 on the Nvidia Jetson Nano.
Every sub dataset of UTiris we evaluated, and the accuracy rate verified the
measurement of the recognition learning model.

Fig 12. The curve plot for the experimental result of 5-fold cross validation.
The X-axis displays the number of epochs ranging from 0 to 16, and the Y-axis
displays the validation accuracy from 0 to 110. Curve plot for the categorical
accuracy for 17 epochs. The accuracy was defined as the total frequency of the
correct acceptance and the imitators with correct rejection.

The final curve of validation accuracy and loss were used to evaluate the
proposed framework, as displayed in Fig 12. Through the training of the
proposed learning model with 17 epochs, all experimental results are
visualized by the Tensorboard tool. The figures of Fig 12(A) and Fig 12(C)
illustrated the accuracy is proportional to the increasing epochs and remains
steady accuracy after the 12th epoch. In each cross-validation, we chose the
highest accuracy as the benchmark to represent the best performance of our
fine-tuned learning model. In particular, the experimental results are
presented in Fig 12(A), it manifests that while mobile inception V4 is applied
on VW session, the best result was achieved by the fine-tuned method with the
accuracy of 99.37% and peaked at 100% recognition rate in 5th cross-validation
of 11th epoch on NIR session. Here, the difference between the average
accuracy of the proposed framework with VW and NIR was 0.3%. Gradually, with
the help of optimization functions, loss function learned to reduce the error
in prediction. The results showed that the average loss rate is decreased by
roughly 4%, from 4.05% to 0.14%. We found that increasing the number of
training iterations could significantly improve the proposed learning
framework performance. Comparative analysis indicates the experimental results
could be acceptable for testing models that could be as close as possible to
the performance; this is because the cross-validation takes effectively use of
the generated data.

##  5 Discussion

In this study, an iris authentication system based on Nvidia Jetson Nano was
introduced. The proposed system consists of two distinct functions, iris
detection and recognition. We consider the iris database, which consists of 2
classes. The dataset is split into training data and testing data set and
organized into 2 folders, train and test. The 5- fold cross-validations are
calculated with a batch size of 8 for 17 epochs, demonstrating that the fine-
tuned mobile Inception V4 model was steady in every validation phase, as
explained in Tables 1 and 2.

Table 1: The table presents the intercorrelations among the 5-fold cross-
validations on VW session.

Fine-tuned Inception V4 |  Epoch 0 |  Epoch 1 |  Epoch 2 |  Epoch 3 |  Epoch 4 |  Epoch 5 |  Epoch 6 |  Epoch 7 |  Epoch 8 |  Epoch 9 |  Epoch 10 |  Epoch 11 |  Epoch 12 |  Epoch 13 |  Epoch 14 |  Epoch 15 |  Epoch 16 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Cross Validation 1 |  3.163 |  20.89 |  49.37 |  68.35 |  82.28 |  87.97 |  91.77 |  93.04 |  94.30 |  95.57 |  95.57 |  96.20 |  98.71 |  96.84 |  98.10 |  97.47 |  97.47 |
Validation Loss |  4.347 |  3.584 |  2.691 |  1.865 |  1.247 |  0.8529 |  0.5879 |  0.4328 |  0.3355 |  0.3285 |  0.2723 |  0.2568 |  0.1840 |  0.2100 |  0.1538 |  0.1724 |  0.1787 |
Cross Validation 2 |  17.72 |  37.34 |  58.86 |  76.58 |  85.44 |  91.14 |  94.30 |  95.57 |  94.94 |  95.57 |  97.47 |  97.47 |  97.47 |  98.37 |  98.73 |  98.10 |  98.10 |
Validation Loss |  3.947 |  3.095 |  2.354 |  1.652 |  1.146 |  0.7699 |  0.4740 |  0.3398 |  0.3281 |  0.2530 |  0.1713 |  0.1869 |  0.1559 |  0.1331 |  0.1291 |  0.1373 |  0.1269 |
Cross Validation 3 |  20.25 |  34.18 |  53.80 |  70.89 |  84.18 |  86.71 |  92.41 |  95.57 |  91.77 |  94.94 |  98.10 |  97.47 |  98.73 |  97.47 |  98.10 |  99.37 |  98.73 |
Validation Loss |  3.940 |  3.164 |  2.489 |  1.753 |  1.217 |  0.8743 |  0.5731 |  0.4074 |  0.4083 |  0.2827 |  0.1922 |  0.1877 |  0.1621 |  0.1836 |  0.1559 |  0.1482 |  0.1365 |
Cross Validation 4 |  14.01 |  36.93 |  57.32 |  78.34 |  84.71 |  91.08 |  91.08 |  94.90 |  94.27 |  98.09 |  97.45 |  97.45 |  98.73 |  96.82 |  97.45 |  98.73 |  98.73 |
Validation Loss |  4.073 |  3.067 |  2.219 |  1.596 |  1.054 |  0.7190 |  0.5629 |  0.3877 |  0.3307 |  0.1651 |  0.1555 |  0.1583 |  0.1314 |  0.1817 |  0.1509 |  0.1040 |  0.1358 |
Cross Validation 5 |  22.67 |  33.33 |  62.67 |  77.33 |  83.33 |  87.33 |  92.00 |  92.67 |  95.33 |  96.00 |  97.33 |  96.00 |  98.67 |  96.00 |  97.33 |  98.00 |  98.00 |
Validation Loss |  3.879 |  3.133 |  2.200 |  1.593 |  1.031 |  0.7751 |  0.5743 |  0.4867 |  0.2955 |  0.2077 |  0.1971 |  0.2256 |  0.1518 |  0.1867 |  0.1798 |  0.1435 |  0.1333 |

Table notes that all the accuracy rates/% and loss rates/% of 17 epochs
measures on the proposed framework in columns, and the rows show five
iterations for each k cross-validation(k=5).

Table 2: The table presents the intercorrelations among the 5-fold cross-
validations on NIR session.

Fine-tuned Mobile Inception V4 |  Epoch 0 |  Epoch 1 |  Epoch 2 |  Epoch 3 |  Epoch 4 |  Epoch 5 |  Epoch 6 |  Epoch 7 |  Epoch 8 |  Epoch 9 |  Epoch 10 |  Epoch 11 |  Epoch 12 |  Epoch 13 |  Epoch 14 |  Epoch 15 |  Epoch 16 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Cross Validation 1 |  15.29 |  38.85 |  61.78 |  78.34 |  86.62 |  90.45 |  92.99 |  94.27 |  94.90 |  93.63 |  96.18 |  96.18 |  98.73 |  99.36 |  98.09 |  98.09 |  96.18 |
Validation Loss |  3.958 |  3.083 |  2.254 |  1.488 |  1.046 |  0.6934 |  0.5036 |  0.4207 |  0.3747 |  0.3080 |  0.2304 |  0.2041 |  0.1837 |  0.1500 |  0.1783 |  0.1879 |  0.1806 |
Cross Validation 2 |  14.10 |  27.56 |  55.13 |  79.49 |  83.33 |  92.95 |  92.95 |  93.95 |  96.79 |  95.51 |  96.15 |  96.79 |  97.44 |  98.72 |  98.08 |  99.36 |  98.72 |
Validation Loss |  4.061 |  3.282 |  2.411 |  1.710 |  1.236 |  0.8172 |  0.5813 |  0.4011 |  0.2987 |  0.2820 |  0.2378 |  0.1842 |  0.1670 |  0.1650 |  0.1443 |  0.1249 |  0.1407 |
Cross Validation 3 |  8.91 |  33.12 |  56.05 |  74.52 |  84.08 |  91.72 |  92.99 |  93.63 |  97.45 |  98.09 |  97.45 |  96.82 |  98.73 |  99.36 |  98.09 |  96.82 |  97.45 |
Validation Loss |  4.061 |  3.237 |  2.437 |  1.695 |  1.179 |  0.7826 |  0.5643 |  0.4510 |  0.2988 |  0.2027 |  0.2073 |  0.2127 |  0.1461 |  0.1474 |  0.1594 |  0.1854 |  0.1444 |
Cross Validation 4 |  11.25 |  34.38 |  68.13 |  77.50 |  84.38 |  90.00 |  95.00 |  93.75 |  97.50 |  98.13 |  98.13 |  95.63 |  98.75 |  96.88 |  96.88 |  98.75 |  98.13 |
Validation Loss |  4.124 |  3.183 |  2.256 |  1.657 |  1.125 |  0.7234 |  0.4915 |  0.3818 |  0.2901 |  0.2054 |  0.1326 |  0.2061 |  0.1663 |  0.2056 |  0.1669 |  0.1421 |  0.1258 |
Cross Validation 5 |  7.59 |  37.34 |  61.39 |  78.48 |  81.65 |  95.51 |  91.14 |  92.41 |  91.77 |  98.10 |  98.10 |  100 |  98.73 |  99.37 |  100 |  99.37 |  98.10 |
Validation Loss |  4.134 |  3.319 |  2.374 |  1.681 |  1.192 |  0.7588 |  0.6120 |  0.4739 |  0.3495 |  0.2026 |  0.1707 |  0.1466 |  0.1456 |  0.1354 |  0.1218 |  0.1355 |  0.1128 |

Table notes that all the accuracy rates/% and loss rates/% of 17 epochs
measures on the proposed framework in columns, and the rows show five
iterations for each k cross-validation(k=5).

In the linear layer of the fine-tuned mobile Inception V4 architecture, we
propose a module of three adaptive average pooling layers with various
properties and sizes. This module emphasizes the down sampling of the overall
feature information, mainly include two positive roles: 1) while reducing the
parameters, it is more reflected in the complete transmission of information.
2) more useful discriminative information is passed to the next layer for the
feature extraction while reducing the dimension. These changes effectively
speed up the fitting of depth network and data, and it decreases considerable
computing costs so that our fine-tuned model can achieve successive and ideal
recognition rates in the first few epochs. Fig 9 illustrates an example of the
stacked pooling, where the kernel set K ={3, 2, 1} and the stride s = 1. In
empirical studies, this configuration shows the best performance in most
cases. Although these stacked layers increase the computational complexity of
the proposed model, the research still demonstrates that the complex
combination of pooling layers can significantly improve the recognition rate
of the learning model more quickly in the neural network. Collectively, the
proposed Mask R-CNN model has delivered a venerable of 99.99% detection rate.

Considering the results in Tables 1 and 2, we can observe that increasing the
number of training epochs can slightly improve the recognition rate. In the
initial training phase, the recognition accuracy of the model in both sessions
is relatively low. This is because the model needs to learn from the training
data, whereas the sample of training data has not fitted the proposed model.
On the whole, the average accuracy of VW sessions (15.56%) is higher than the
NIR session (11.43%) in the first epoch since the color images contain a large
amount of different discrete information in three channels. In the 8th epoch,
the curve shows that the performance of the model on the NIR session (93.01%)
is smoothly beyond than the VW session (92.31%). Based on the comparative
analysis of two models, this research reveals the fact that the convergence
speed of our fine-tined Inception V4 processing on the NIR session, which is
better than the VW session in overall epochs.

At the same time, we observe that the validation accuracy would fluctuate
after 10th epochs. The reason for this is that the amount of data in the
experimental data set is still relatively small, and the training on the model
with more layers may bring some problems like overfitting due to the various
size of pre-processed iris images and the effective recognition area within
the range of each pre-processed iris image is disparate. Under the mobile
environment, this seems inevitable. To solve this problem, we used the early
stopping method that calculated the accuracy of validation data at the end of
each epoch and stopped training when the accuracy is no longer improved.

We also investigate how to train a fine-tuned CNN that can classify iris
images with high accuracy. The hyperparameters of the learning rate are
closely associated with model performance. Weight decay can be effective at
preventing the problem of over-fitting. They control the size of network
parameters updated after each iteration. Our experiment shows that when the
learning rate is modulated by 1e-4, the proposed framework has a notable
recognition rate of 100% in the 5th verification set of VW session.
Comparatively, the accuracy rate of assessment is over 99% in NIR session.
Likewise, we also try to use the learning rate of 1e-3 and 1e-5, whereas this
leads to opposing effects of slow training or early occurrence of overfitting
phenomenon because of an inappropriate learning rate and the insufficient
training data [68].

Lastly, compared to preprocessing and recognition approaches used by authors
[17] [19] [20] in Table 3, we make the framework have more agility and
flexible space to adapt the change of system, and research demonstrates the
effectiveness of the proposed framework. Indeed, degradation in image quality
negatively impacts the performance of iris authentication systems, especially
for the infrared images. Starting with good quality images, we do not adopt
any degradation operation in our preprocessing phase. Our Mask R-CNN
architecture is reconstructed to implement the robust detection and the scale-
variant feature extraction, respectively. The model has the advantage that
image space is unaffected by the image degradation process. Furthermore, our
proposed recognition model of fine-tuned mobile Inception V4 always gets top
performance for all levels of methodologies, demonstrating the robust
adaptation and excellent performance and in iris detection, and the highest
performance for iris recognition. It has been observed from the results that
our framework provides the best average recognition accuracy of 98.97% and
99.24% for the VW and NIR session, respectively. The overall accuracy of the
model in the UTiris dataset is 99.10%, are summarized as follows.

Table 3: Comparing the original results obtained from experiments with the
state-of-the-art methodologies. The best results in bold.

References |  Preprocessing |  Localization Precision |  Methodology |  Recognition Accuracy /% |
---|---|---|---|---|---
Bhagyashree Deshpande and Deepak Jayaswal [17] |  Daugman’s integro-differential + Daugman’s Rubber Sheet + 1D log Gabor filter |  × |  Hamming Distance |  95.00% (VW) |
Mohammed Hamzah Abed [19] |  Circular Hough transform + Haar wavelet transform + PCA |  98.73% |  Cosine Distance |  91.14% (NIR) |
Onkar Kaudki and Kishor Bhurchandi [20] |  Rubber-Sheet Unwrapping + Haar wavelet transform |  96.21% |  Hamming Distance |  97.00% (NIR) |
Proposed Frameworks |  Custom Mask-RCNN |  99.99% |  Fine-tuned Inception V4 |  Accuracy/% |
|  |  |  Session
|  VW — NIR |
|  |  |  CV 1
|  98.71% 98.73% |
|  |  |  CV 2
|  99.37% 99.36% |
|  |  |  CV 3
|  99.37% 99.36% |
|  |  |  CV 4
|  98.73% 98.75% |
|  |  |  CV 5
|  98.67% 100% |
|  |  |  Average accuracy |  98.97% 99.24% |
|  |  |  Overall accuracy |  99.10%
|

Following the analysis mentioned above, the result data reveal fewer standard
errors and estimates proposed variables to significant, and the proposed
method is quite superior to the traditional algorithm in the recognition rate.
The reason is that the CNN method has a strong dependence on data and contains
prior knowledge of the pre-training model, and proposed framework can enhance
the robustness of data preprocessing. The experimental results show that the
selected hyperparameters that are suitable for iris recognition. These
optimizations make the proposed framework have a better recognition effect.

##  6 Conclusions

A comprehensive overview of mobile environment devices and an implement of
iris authentication system is presented, and the merits and drawbacks of the
methods used in each study are analyzed. Some of researches operate on images
captured using non-mobile neural network algorithms, resulting in high costs,
low operation speed and complex calculations for mobile terminal devices. This
investigations showed that the method of deep learning is developing towards a
complex hierarchical structure. We develop a learning framework with multi-
modalities for detection, extraction, normalization and recognition for high-
resolution iris images in our iris authentication system. Considering the
diversity of iris samples, the proposed Mask R-CNN architecture applies the
local iris ground truth to achieve the robust iris localization efficiently,
and the zero-padding layer can deal with the complex and varying scale-variant
features flexibly. Thus the whole framework also shows the dynamic and
flexible characteristics in the prepossessing. In addition, an approach of
fined tuned-based Inception V4 architecture to recognize the iris is
introduced, which is tested by 5-fold cross validation with a real data set.
Our experiment results for the proposed framework indicates that the
performance is better than the state-of-the-art methodologies in detection
capability, recognition rate, and the robustness of the system.

In consequence, qualitative and quantitative research designs were adopted to
provide experimental results and attempts to explain the scale-variant
features of models learning through iris recognition. The proposed solutions
are suitable for high performance built-in GPU mobile devices and help
researchers estimate analysis results for further research in the mobile
environment.

## Acknowledgments

We thank the support of the Multimedia Department of Computer Science and
Information Technology at the Putra Malaysia University, and the laboratory of
Computer-Assisted Surgery and Diagnostic (CASD). This research was undertaken
at CASD, and the support of CASD is gratefully acknowledged. I would like to
thank all the contributors to this study for their prompt responses to
different opinions, their continued support and encouragement and the vibrant
research atmosphere that they have provided.

## Author Contributions

Siming Zheng: Conceptualization, Data curation, Investigation, Methodology,
Formal analysis, Visualization, Writing – original draft, Writing – review &
editing.
Rahmita Wirza O.K. Rahmat: Conceptualization, Project administration,
Supervision, Funding acquisition, Writing & original draft.
Fatimah Khalid: Formal analysis, Methodology, Validation, Formal analysis,
Writing – review & editing.
Nurul Amelina Nasharuddin: Data curation, Software, Resources, Visualization,
Writing – review & editing.

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