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

Yilong Zhang; Shichang Yu; Shiliang Pu; Yingyu Wang; Kanlei Wang; Haohao Sun; Haixia Wang

doi:10.1016/j.heliyon.2023.e20052

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

Heliyon. 2023 Sep 14;9(9):e20052. doi: 10.1016/j.heliyon.2023.e20052. eCollection 2023 Sep.

Authors

Yilong Zhang¹, Shichang Yu¹, Shiliang Pu², Yingyu Wang², Kanlei Wang², Haohao Sun³, Haixia Wang¹

Affiliations

¹ College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China.
² Hikvision Research Institute, Hangzhou, 310023, China.
³ College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Abstract

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.