{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:01:07Z","timestamp":1760058067978,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the development of biometric recognition technology, the technology of vein-based verification has garnered growing interest within the domain of biometric recognition. Nonetheless, the difficulties in differentiating between the background and the vein patterns, as well as the multi-branching, irregularity, and high-precision requirements of the vein structure, often make it difficult to achieve high precision and speed in vein mask extraction. To address this problem, we propose HUnet++, a novel vein recognition method based on the symmetric network structure of the Unet++ model, which enhances the speed of vein mask extraction while maintaining accuracy. The HUnet++ model consists of two main parts: a Feature Capture (FC) module for hierarchical feature extraction, and a Feature Fusion (FF) module for multi-scale feature integration. This structural design bears a striking resemblance to the symmetrical architecture of the Unet++ model, playing a crucial role in ensuring the balance between feature processing and integration. Experimental results show that the proposed method achieves precision rates of 91.4%, 84.1%, 78.07%, and 89.5% on the manually labeled dataset and traditionally labeled datasets (SDUMLA-HMT, FV-USM, Custom dataset), respectively. For a single image with a size of 240 pixels, the feature extraction time is 0.0131 s, which is nearly twice as fast as the original model.<\/jats:p>","DOI":"10.3390\/sym17030420","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T08:59:52Z","timestamp":1741683592000},"page":"420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HUnet++: An Efficient Method for Vein Mask Extraction Based on Hierarchical Feature Fusion"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3909-470X","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Energy and Electrical Engineering, Changchun University of Science and Technology, Changchun 130013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5751-9151","authenticated-orcid":false,"given":"Yujiao","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 100096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4950-0266","authenticated-orcid":false,"given":"Xiaofan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 100096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2021.10.004","article-title":"Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities","volume":"79","author":"Shaheed","year":"2022","journal-title":"Inf. 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