{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:18Z","timestamp":1729225698250,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Iris recognition has gained significant attention in identity verification due to the unique, stable texture patterns in iris. Successfully extracting these patterns is essential for quick and precise identification. Although deep learning methods have automated the iris recognition, they predominantly rely on real-valued networks that overlook the complex-valued representation of iris texture. This means they cannot effectively process phase and amplitude information, and fail to integrate domain-specific knowledge of iris, thereby not fully capturing the intricate details of the iris texture. Inspired by classical manual methods that efficiently harness the complex-valued representation of the iris to extract both amplitude and phase information. We integrate Gabor filters with complex-valued neural networks, propose a Complex-Valued Gabor-Attention Residual Fusion Network (GRFN) tailored for iris recognition, aiming to comprehensively capture the iris texture\u2019s multi-scale and multi-orientation phase and amplitude features. The GRFN incorporates adaptive Gabor Complex-Valued Convolution Kernels (GCVK) to introduce a Gabor attention mechanism focused on iris biometric characteristics. Furthermore, we propose a novel residual feature fusion approach that selects and merges local and global features across multiple directions and scales, mitigating model degradation and enhancing the network\u2019s ability to extract iris texture features effectively. Extensive experiments show that the proposed network outperforms the state-of-the-art performance on two benchmark datasets.<\/jats:p>","DOI":"10.3233\/faia240487","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:39:12Z","timestamp":1729168752000},"source":"Crossref","is-referenced-by-count":0,"title":["Complex-Valued Gabor-Attention Residual Fusion Network for Iris Recognition"],"prefix":"10.3233","author":[{"given":"Zhuoru","family":"Li","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, 300450, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]},{"given":"Jian","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, 300450, China"}]},{"given":"Xiaowei","family":"Bai","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, 100071, China"},{"name":"Intelligent Game and Decision Laboratory, Beijing, 100071, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Peking University, Beijing, 100071, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]},{"given":"Yingxi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, Peking University, Beijing, 100071, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]},{"given":"Zhenyu","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xian, 710000, China"}]},{"given":"Liang","family":"Xie","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, 100071, China"},{"name":"Intelligent Game and Decision Laboratory, Beijing, 100071, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]},{"given":"Ye","family":"Yan","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, 100071, China"},{"name":"Intelligent Game and Decision Laboratory, Beijing, 100071, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]},{"given":"Erwei","family":"Yin","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, 100071, China"},{"name":"Intelligent Game and Decision Laboratory, Beijing, 100071, China"},{"name":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, 300450, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240487","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:39:12Z","timestamp":1729168752000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240487","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}