{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:41:48Z","timestamp":1773193308059,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62202322"],"award-info":[{"award-number":["62202322"]}]},{"name":"National Natural Science Foundation of China","award":["19KJA550002"],"award-info":[{"award-number":["19KJA550002"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["62202322"],"award-info":[{"award-number":["62202322"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["19KJA550002"],"award-info":[{"award-number":["19KJA550002"]}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["62202322"],"award-info":[{"award-number":["62202322"]}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["19KJA550002"],"award-info":[{"award-number":["19KJA550002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.<\/jats:p>","DOI":"10.3390\/s24041132","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T03:53:46Z","timestamp":1707450826000},"page":"1132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism"],"prefix":"10.3390","volume":"24","author":[{"given":"Meihui","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"},{"name":"Jiangsu Engineering Laboratory of Cyberspace Security, Suzhou 215006, China"}]},{"given":"Yufei","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Zhaohui","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"},{"name":"Jiangsu Engineering Laboratory of Cyberspace Security, Suzhou 215006, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mulyono, D., and Jinn, H.S. 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