A Central Point-based Analysis for Fingerprint Liveness Detection

Min Young Lim, Tae Yong Kim, Joung Eun Park, Pyo Min Hong, Youn Kyu Lee

Abstract: To minimize security threats using fake fingerprints, various CNN-based fingerprint liveness detection methods have been proposed. However, since existing methods mainly employ a random crop method, key information of target fingerprint may be overlooked during the training process, which may lead to decreased detection accuracy. In this paper, we propose a new fingerprint liveness detection method based on central point analysis of fingerprints. The proposed method measures a central point of target fingerprint, extracts the crops with different sizes based on the central point, and fuses the liveness scores inferred from each crop-size model. As a result of validating our method using real datasets, it was confirmed that our method effectively detects fake fingerprints compared to existing methods.