{"id":13174,"date":"2026-10-17T05:20:55","date_gmt":"2026-10-16T21:20:55","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=13174"},"modified":"2026-10-17T05:47:12","modified_gmt":"2026-10-16T21:47:12","slug":"jase-202701-36-028","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-028","title":{"rendered":"Improved MobileNetV2 for Finger Vein Recognition"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Hongcheng Zhou1<\/sup> and Aikang Liu2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>School of Electronic Information Engineering, Jinling Institute of Technology, Nanjing 211169, China<\/p>\n\n\n\n

2<\/sup>School of Software Engineering, Jinling Institute of Technology, Nanjing 211169, China<\/p>\n<\/div>\n\n\n\n

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Received: August 04, 2026
Accepted: September 09, 2026
Publication Date: October 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

SE Channel-Attention Module <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.028<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Finger vein recognition is a promising biometric technology for identity authentication, but practical systems are often constrained by limited computing and storage resources. To improve the recognition performance of lightweight models under these constraints, this paper proposes PSE-MobileNetV2, a MobileNetV2-based finger vein recognition network with partially embedded Squeeze-and-Excitation (SE) channel-attention blocks. Instead of adding SE blocks to every inverted residual stage, the proposed model inserts them only into stages 4-7, where the feature maps encode higher-level vein representations. This design strengthens channel-wise feature recalibration while avoiding unnecessary early-stage computational overhead. In the reported closed-set, image-disjoint classification experiments on the FV-USM and SDUMLA public datasets, PSE-MobileNetV2 achieves accuracies of 99.49% and 97.06%, respectively, exceeding the MobileNetV2 baseline by 0.31 and 0.84 percentage points. These results suggest that selective SE placement can improve the accuracy-efficiency trade-off of MobileNetV2. However, the current study does not establish verification performance or statistical significance across repeated runs.<\/p>\n\n\n\n

Keywords: Deep Learning; Convolutional Neural Network; Finger Vein Recognition; Channel Attention; MobileNetV2; Lightweight Network; Biometric Recognition<\/em><\/p>\n\n\n\n

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