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This study explores the impact of blinking on deep learning based iris recognition, addressing a critical aspect in the development of robust, reliable, and non-intrusive biometric systems. While previous research has demonstrated the promise of Convolutional Neural Networks (CNNs), such as AlexNet, GoogleLeNet, and ResNet, the impact of blinking remains underexplored in this context. To address this gap, our research focuses on training multiple ResNet models with varying degrees of iris occlusion exposure. Using a dataset with 101 subjects, we generated cohorts of synthetically occluded images ranging from 0% occlusion to 90% occlusion. Our findings reveal a noteworthy linear performance decrease in models unexposed to blinked images as iris occlusion increases. However, augmenting the training dataset with occluded images significantly mitigates this performance degradation, highlighting the importance of accounting for blinking in the development of reliable iris recognition systems. 

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.