3D CNN-based fingerprint anti-spoofing through optical coherence tomography

• Published in: Heliyon Vol. 9; no. 9; p. e20052
• Main Authors: Zhang, Yilong, Yu, Shichang, Pu, Shiliang, Wang, Yingyu, Wang, Kanlei, Sun, Haohao, Wang, Haixia
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023; Elsevier
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e20052

Optical coherence tomography (OCT) is a noninvasive high-resolution imaging technology that can accurately acquire the internal characteristics of tissues within a few millimeters. Using OCT technology, the internal fingerprint structure, which is consistent with external fingerprints and sweat glands, can be collected, leading to high anti-spoofing capabilities. In this paper, an OCT fingerprint anti-spoofing method based on a 3D convolutional neural network (CNN) is proposed, considering the spatial continuity of 3D biometrics in fingertips. Experiments were conducted on self-built and public datasets to test the feasibility of the proposed anti-spoofing method. The anti-spoofing strategy using a 3D CNN achieved the best results compared with classic networks.