{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:27:52Z","timestamp":1770269272550,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T00:00:00Z","timestamp":1496707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.<\/jats:p>","DOI":"10.3390\/s17061297","type":"journal-article","created":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T10:53:09Z","timestamp":1496746389000},"page":"1297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":153,"title":["Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors"],"prefix":"10.3390","volume":"17","author":[{"given":"Hyung","family":"Hong","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Min","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Kang","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/TIFS.2017.2680403","article-title":"Face Recognition Using Sparse Fingerprint Classification Algorithm","volume":"12","author":"Larrain","year":"2017","journal-title":"IEEE Trans. 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