ARTICLE Huang_IEEETCSVT_2024/IDIAP Mirror-based Full-View Finger Vein Authentication with Illumination Adaptation Huang, Junduan Li, Zifeng Bhattacharjee, Sushil Kang, Wenxiong Marcel, Sébastien Biometrics Finger vein Full-view Authentication Miura-Match Multi-view Vein recognition EXTERNAL http://publications.idiap.ch/attachments/papers/2026/Huang_IEEETCSVT_2024.pdf PUBLIC IEEE Transactions on Circuits and Systems for Video Technology 2024 DOI: 10.1109/TCSVT.2024.3490581 doi Full-view finger vein (FV) biometrics systems capture multiple FV images of the presented finger ensuring that the entire surface of the finger is covered. Existing full-view FV systems suffer from three common problems: large device size, high cost for multi-camera system, and sub-optimal illumination in the recorded FV images. To address the problem of device size, we propose a novel Mirror-based Full-view FV (MFFV) capture device. The MFFV device has a compact size by using mirror-reflection approach. We reduce the cost of the device by using low-cost components, in particular, consumer-grade cameras. To address the problems of lower-quality images captured by such cameras and obtain optimally illuminated FV images, we propose a two-step approach. The first step is a Multi-illumination Intensities FV (MIFV) capture strategy, which capture the FV image set with varying illumination intensities. In the second step, a FV illumination adaptation (FVIA) algorithm is proposed to select the optimally illuminated FV image from the MIFV image set. Using the proposed MFFV device, we collect a comprehensive dataset, namely MFFV dataset, along with reproducible baseline FV authentication results for both single-view and full-view FV. Our experimental results demonstrate that the MIFV capture strategy as well as the FVIA algorithm can effectively improve the authentication performance, and that the full-view FV authentication is significantly superior than the single-view FV authentication. The source-code and dataset for reproducing our experimental results are publicly available (https://github.com/SCUT-BIP-Lab/MFFV).