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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. 

Robust iris recognition performance remains a significant challenge, especially for off-angle images captured in less constrained environments. While convolutional neural networks (CNNs) have shown great promise in iris recognition, there is limited research on the effects of gaze-angle distortions on recognition performance and the development of dedicated frameworks for off angle iris recognition. This study investigates different recognition fusion strategies for left and right off-angle iris images using deep learning. A transfer learning approach leveraging the pre-trained AlexNet model is employed to classify iris images, where frontal-view iris images are used for training and off-angle images for testing. Three fusion strategies are explored: (i) a double model approach with decision-level fusion, where separate models are trained for left and right irises and their predictions are combined, (ii) a single model approach with feature-level fusion, where a unified model extracts and fuses features from both irises, and (iii) a single model approach with image-level fusion, where left and right iris images are merged at the input level. The performance of these methods is evaluated using accuracy as the primary metric to assess the model's generalization capabilities under off-angle conditions. Experimental results highlight the advantages and trade-offs of each fusion strategy, offering insights into the role of bilateral iris information in enhancing recognition performance. The findings of this study contribute to the development of more robust deep learning-based iris recognition systems capable of handling off-angle variations. 

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. 

Iris biometric systems offer non-contact authentication, particularly advantageous in controlled environments such as security checkpoints. However, challenges arise in less controlled scenarios such as standoff biometrics where captured images mostly are non-ideal including off-angle. This paper addresses the need for iris recognition models adaptable to various gaze angles by proposing a blink detection algorithm as an additional feature. The study explores different blink detection methods including involving logistic regression, random forest, and deep learning models. For the first methodology, logistic regression and a random forest model were used to classify eye images into four different blink classes. The second methodology involved labeling eye openness percentage. The ground-truth eye blink was calculated using facial landmarks detected by the MediaPipe model. For the deep learning approach, we used a pre-trained Convolutional Neural Network (CNN) model by replacing the output layer with a regression layer. Results show improved precision and recall when incorporating height and width features for the regression model. The AlexNet model achieves superior performance, reaching 90% accuracy with a 10 % error threshold. This research contributes valuable insights for developing robust iris recognition models adaptable to diverse gaze angles. 

Person identification using biometrics has become a safer and trustworthy mechanism with the advancement of technology. Among all biometric identification methods, iris recognition has achieved very low false acceptance rates due to its complex and unique patterns. The low acceptance rates apply only to frontal iris images. Capturing frontal iris images is not always possible, especially in uncontrolled environments, where most of the iris images captured tend to be non-ideal, such as off-angle images. Off-angle iris images suffer from several issues, including corneal refraction, limbus occlusion, the effect of gaze angle, and depth of field blur. These effects distort the iris patterns, causing the similarity scores between the same individual to widen and scores between different individuals to become closer. This also causes false acceptance rates to increase, as it increases the chances of misclassification. This highlights the need for improving the performance of off-angle iris recognition. By leveraging the low false-acceptance rates of the frontal iris images, we build generated frontal version of the iris images using off-angle iris images and achieved better performance compared with the perspective transformation. We built a modified version of the Pix2Pix GAN to achieve the frontal projection of off-angle iris images. Instead of using a Mean Squared loss function in the Pix2Pix GAN, we use a combination of Mean Squared loss function, Matrix Multiplication loss, and SSIM loss function to generate sharper images that can capture the textural information of the original image better. 

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. 

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.