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1. CNN‐based off‐angle iris segmentation and recognition

   https://doi.org/10.1049/bme2.12052  

   Jalilian, Ehsaneddin; Karakaya, Mahmut; Uhl, Andreas (June 2021, IET Biometrics)

   null (Ed.)

   Accurate segmentation and parameterization of the iris in eye images still remain a significant challenge for achieving robust iris recognition, especially in off‐angle images captured in less constrained environments. While deep learning techniques (i.e. segmentation‐based convolutional neural networks (CNNs)) are increasingly being used to address this problem, there is a significant lack of information about the mechanism of the related distortions affecting the performance of these networks and no comprehensive recognition framework is dedicated, in particular, to off‐angle iris recognition using such modules. In this work, the general effect of different gaze angles on ocular biometrics is discussed, and the findings are then related to the CNN‐based off‐angle iris segmentation results and the subsequent recognition performance. An improvement scheme is also introduced to compensate for some segmentation degradations caused by the off‐angle distortions, and a new gaze‐angle estimation and parameterization module is further proposed to estimate and re‐project (correct) the offangle iris images back to frontal view. Taking benefit of these, several approaches (pipelines) are formulated to configure an end‐to‐end framework for the CNN‐based offangle iris segmentation and recognition. Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off‐angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. Finally, the recognition performance of the selected approach is compared against some state‐of‐the‐art off‐angle iris recognition algorithms. 

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2. Gaze-angle Impact on Iris Segmentation using CNNs

   https://doi.org/10.1109/BTAS46853.2019.9185970  

   Jalilian, E.; Uhl, A.; Karakaya, M. (October 2019, IEEE International Conference on Biometrics Theory Applications and Systems)

   Emerging standoff iris recognition systems operate under unconstrained conditions and the iris images captured by these systems are more subject to off-angle acquisition distortions. While deep learning techniques (e.g. convolutional neural networks (CNNs)) are increasingly becoming a tool of choice for iris segmentation tasks, yet there is a significant lack of information about how these distortions affect the performance of such networks. In this work, we thoroughly discuss the general effect of different gaze angles on ocular biometrics and relate the findings to off-angle iris segmentation using CNNs. In particular, we conduct systematical analysis on the impact of different gaze angles on segmentation performance of two CNNs with different architectures. The networks’ performance turns out to have a direct relation to the closeness of gaze-angles in the training and testing images, and it declines as the gaze angles diverge. We further investigate the effect of (i) increasing the quantity of iris training data in case of gaze angles in training and test data match, and (ii) considering iris training data consisting of several distinct gaze-angles (we obtain promising results using the second configuration). Finally, we compare our results to those of some classical iris segmentation algorithms, where the CNNs are found to outperform the classical algorithms. 

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3. Off-angle iris recognition using single and multiple deep learning frameworks

   https://doi.org/10.1117/12.2662862  

   Chavarro, David; Karakaya, Mahmut (June 2023, Multimodal Image Exploitation and Learning)

   Agaian, Sos S.; DelMarco, Stephen P.; Asari, Vijayan K. (Ed.)

   Iris recognition is a widely used biometric technology that has high accuracy and reliability in well-controlled environments. However, the recognition accuracy can significantly degrade in non-ideal scenarios, such as off-angle iris images. To address these challenges, deep learning frameworks have been proposed to identify subjects through their off-angle iris images. Traditional CNN-based iris recognition systems train a single deep network using multiple off-angle iris image of the same subject to extract the gaze invariant features and test incoming off-angle images with this single network to classify it into same subject class. In another approach, multiple shallow networks are trained for each gaze angle that will be the experts for specific gaze angles. When testing an off-angle iris image, we first estimate the gaze angle and feed the probe image to its corresponding network for recognition. In this paper, we present an analysis of the performance of both single and multimodal deep learning frameworks to identify subjects through their off-angle iris images. Specifically, we compare the performance of a single AlexNet with multiple SqueezeNet models. SqueezeNet is a variation of the AlexNet that uses 50x fewer parameters and is optimized for devices with limited computational resources. Multi-model approach using multiple shallow networks, where each network is an expert for a specific gaze angle. Our experiments are conducted on an off-angle iris dataset consisting of 100 subjects captured at 10-degree intervals between -50 to +50 degrees. The results indicate that angles that are more distant from the trained angles have lower model accuracy than the angles that are closer to the trained gaze angle. Our findings suggest that the use of SqueezeNet, which requires fewer parameters than AlexNet, can enable iris recognition on devices with limited computational resources while maintaining accuracy. Overall, the results of this study can contribute to the development of more robust iris recognition systems that can perform well in non-ideal scenarios. 

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4. How Eye Blink Affects the Recognition Performance of Traditional Off-Angle Iris Images

   https://doi.org/10.1109/SoutheastCon52093.2024.10500074  

   Headley, Eli; Tebor, Daniel; Karakaya, Mahmut (March 2024, IEEE)

   Iris recognition is known to be a robust biometric method, leveraging distinctive iris patterns for reliable identification. However, challenges arise in traditional approaches, especially in non-ideal images captured in environments where subjects can freely move around. This research focuses on the impact of eyelid occlusion on off-angle iris recognition within a traditional framework. The primary objectives are to i) assess the impact of eye blink on overall recognition performance; ii) explore the effects on each specific angle; and iii) examine the occlusion level required to maintain an acceptable recognition performance in a traditional iris recognition pipeline. To generate different levels of eyelid occlusion, we manipulated the eyelid segmentation results to mimic eye blinking due to lack of adequate amount of eye blinking images in publicly available datasets. We conducted three sets of experiments using frontal and off-angle iris images from 100 different subjects. Based on the results, up to a certain occlusion level (60%), eyelid occlusion enhances performance for severe off-angle images by masking distortion-prone iris portions. This improvement comes from the masking of non-ideal portions of the off-angle iris images that are distorted by the refraction of light through the cornea caused by the gaze angle. This insight offers potential improvements for traditional iris recognition, highlighting the nuanced interplay of occlusion and gaze angle on recognition performance. 

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5. Comparison of elliptical unwrapping and frontal projection for off-angle iris normalization

   https://doi.org/10.1117/12.2588898  

   Ehrlich, Emily; Karakaya, Mahmut (April 2021, Disruptive Technologies in Information Sciences V)

   Blowers, Misty; Hall, Russell D.; Dasari, Venkateswara R. (Ed.)

   Iris recognition is one of the most accurate biometric recognition techniques, however off-angle iris recognition has yet to have an established comprehensive recognition framework. This is due to the difficulties in the recognition of off-angle iris image inconsistencies within the iris patterns when gaze deviations are present. In this work, we investigate different iris normalization techniques and compare their performance. The two methods under investigation include elliptical normalization and circular normalization after frontal projection of off-angle iris recognition. Elliptical normalization samples the iris texture using elliptical segmentation parameters: 𝑥, 𝑦, 𝑟1 , 𝑟2 , θ where 𝑥, 𝑦 are coordinates, 𝑟1, 𝑟2 are the radius, and θ is the orientation. Also, when investigating circular unwrapping, we will be using the ellipse segmentation parameters to estimate the gaze deviation. The image will be projected back to a frontal view using perspective transformation. Then, we segment the transformed image and normalize using the circular parameters: 𝑥, 𝑦, 𝑟 where 𝑥, 𝑦 are coordinates and r is the radius. We further investigate if: (i) elliptical normalization or circular unwrapping recognition performance is higher, and (ii) if the two segmentation methods in circular unwrapping increase the recognition efficiency 

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