Deep belief network based statistical feature learning for fingerprint liveness detection

• This work presents local discriminative feature learning for fingerprint liveness detection.
• Our method does not require specific knowledge regarding live or fake fingerprints.
• Our method does not require specific knowledge on recognition systems.
• Our method can be performed with relatively low computation complexity.
• Our method achieves good accuracy on various sensor datasets of the LivDet2013 test.

Fingerprint recognition systems are vulnerable to impersonation by fake or spoof fingerprints. Fingerprint liveness detection is a step to ensure whether a scanned fingerprint is live or fake prior to a recognition step. This paper presents a fingerprint liveness detection method based on a deep belief network (DBN). A DBN with multiple layers of restricted Boltzmann machine is used to learn features from a set of live and fake fingerprints and also to detect the liveness. The proposed method is a systematic application of a deep learning technique, and does not require specific domain expertise regarding fake fingerprints or recognition systems. The proposed method provides accurate detection of the liveness with various sensor datasets collected for the international fingerprint liveness detection competition.