{'fraction_non_alphanumeric': 0.027834351663272233, 'fraction_numerical': 0.006109979633401222, 'mean_word_length': 5.464912280701754, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Lightweight iris segmentation network for low-power devices

Abstract. With the increasing demand for authentication using iris biometrics, iris recognition systems are gradually being deployed on low-power devices. Iris segmentation is a critical step for iris recognition systems. However, existing iris segmentation methods often require a more complex network to obtain higher accuracy, which makes the segmentation model have a greater number of parameters and a higher computation cost. Therefore, these algorithms are not suitable for deployment on low-power devices. To address this problem, we propose a lightweight iris segmentation network, referred to as ATTention Linknet. First, we redesign the feature extraction part of the Linknet, which combines traditional convolution based on an attention mechanism with depthwise separable convolution to reduce computation cost while maintaining accuracy. Then, a proposed loss function that better segments the iris images taken under different cross-sensors is used to train the network. Experimental results on the Chinese Academy of Sciences (CASIA-V4), Indian Institute of Technology Delhi (IITD), and the University of Beira Interior (UBIRIS.V2) iris databases show that the proposed method better balances iris segmentation precision and efficiency compared with state-of-the-art iris segmentation methods. Specifically, our model obtains an error rate of 1.47% on the CASIA-V4 database with 1.69 M parameters.