Revolutionizing Iris Segmentation: Unleashing the Power of Fast and Reliable Multi-Spectral Analysis Through Deep Learning Implementation
Abstract
Iris recognition techniques have gained significant attention in the field of identification due to their high performance and robustness. The unique texture of the iris makes it an ideal biometric for individual identification. However, there are various challenges that arise in real-world recognition scenarios. This thesis focuses on different phases of the iris recognition system and reviews the methods associated with each phase. The recognition system is composed of seven phases: acquisition, preprocessing, segmentation, normalization, feature extraction, feature selection, and classification. In the acquisition phase, iris images are captured, followed by preprocessing to enhance the quality of the images. The segmentation phase involves separating the iris region from the background, and the normalized iris region is shaped into a rectangle in the normalization phase. Features of the iris region are then extracted in the feature extraction phase, and unique features are selected using feature selection techniques in the feature selection phase. Finally, the iris images are classified in the classification phase. The thesis also discusses two approaches to iris recognition: the traditional approach and the deep learning approach. Iris segmentation is a critical step in iris recognition systems and has a direct impact on authentication and recognition results. However, standard segmentation techniques may not perform well in noisy iris databases captured under challenging conditions. Moreover, the lack of large iris databases hinders the performance improvement of convolutional neural networks. The proposed method addresses these challenges by effectively handling irregular iris images captured under visible light.
Keywords
Iris Segmentation, Multi-Spectral Analysis, Biometric Technology, Image Processing, Segmentation Techniques