Binary vs. multi-class segmentation for off-angle iris images using deep learning frameworks

Abstract

During the pandemic, it is a critical task to recognize individuals and to verify their identity without touching a surface or removing the face mask. Compared with other biometric modalities, iris recognition provides accurate, reliable, and contactless biometrics measure. Traditional iris recognition systems require high quality frontal iris images. The image quality dependency limits its recognition performance in standoff applications. However, standoff biometric systems work in a less controlled environment where the captured images may be nonideal and off-angle. Since segmentation is the first step among recognition tasks, having an accurate segmentation is extremely critical to achieving a high recognition performance especially for off-angle iris images. Recent advances in deep learning enable the usage of some convolutional neural networks (CNN) for the challenging iris segmentation task. During training process, binary iris segmentation masks feed to the CNN framework to learn the iris texture where all other eye structures included in the same class. However, the pupil and sclera segmentation may provide useful additional information for iris segmentation. In this paper, we investigate the CNN-based iris segmentation frameworks for binary segmentation and multi-class segmentation. We first train the deep networks with binary segmentation masks (iris vs. others). Then, additional deep networks are trained with multi-class segmentation masks where pupil, iris texture, sclera, and other eye structures in separate classes. Finally, we compare the segmentation accuracies with off-angle iris images where images are captured from -50° to 50° in angle. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.