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Iris recognition is a reliable biometric identification method known for its low false-acceptance rates. However, capturing ideal iris images is often challenging and time-consuming, which can degrade the performance of iris recognition systems when using non-ideal images. Enhancing iris recognition performance for non-ideal images would expedite and make the process more flexible. Off-angle iris images are a common type of non-ideal iris images, and converting them to their frontal version is not as simple as making geometric transformations on the off-angle iris images. Due to challenging factors such as corneal refraction and limbus occlusion, frontal projection requires a more comprehensive approach. Pix2Pix generative adversarial networks (GANs), with their pairwise image conversion capability, provide the ideal foil for such a tailored approach. We demonstrate how Pix2Pix GANs can effectively be used for the problem of converting off-angle iris images to frontal iris images. We provide a comprehensive exploration of techniques using Pix2Pix GAN to enhance off-angle to frontal iris image transformation by introducing variations in the loss functions of Pix2Pix GAN for better capturing the iris textures and the low contrast, changing the medium of input from normalized iris to iris codes, and ultimately delving deeper into studying which regions of the Gabor filters contribute the most to iris recognition performance. 

Iris recognition is one of the well-known areas of biometric research. However, in real-world scenarios, subjects may not always provide fully open eyes, which can negatively impact the performance of existing systems. Therefore, the detection of blinking eyes in iris images is crucial to ensure reliable biometric data. In this paper, we propose a deep learning-based method using a convolutional neural network to classify blinking eyes in off-angle iris images into four different categories: fully-blinked, half-blinked, half-opened, and fully-opened. The dataset used in our experiments includes 6500 images of 113 subjects and contains images of a mixture of both frontal and off-angle views of the eyes from -50 to 50 in gaze angle. We train and test our approach using both frontal and off-angle images and achieve high classification performance for both types of images. Compared to training the network with only frontal images, our approach shows significantly better performance when tested on off-angle images. These findings suggest that training the model with a more diverse set of off-angle images can improve its performance for off-angle blink detection, which is crucial for real-world applications where the iris images are often captured at different angles. Overall, the deep learning-based blink detection method can be used as a standalone algorithm or integrated into existing standoff biometrics frameworks to improve their accuracy and reliability, particularly in scenarios where subjects may blink.