Impact of Blinking on Deep Learning Based Iris Recognition
Daniel Tebor, Eli Headley, Mahmut Özge Karakaya · 2024
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.