On the accuracy and robustness of deep triplet embedding for fingerprint liveness detection
Liveness detection is an anti-spoofing technique for dealing with presentation attacks on biometrics authentication systems. Since biometrics are usually visible to everyone, they can be easily captured by a malignant user and replicated to steal someone's identity. In particular, fingerprints can be easily reproduced by using gummy materials and attached to the impostor's fingertips, making the attack go unnoticed by security personnel and camera networks. In this paper, the classical binary classification formulation (live/fake) is substituted by a deep metric learning framework that can generate a representation of real and artificial fingerprints and explicitly models the underlying factors that explain their inter-and intra-class variations. The framework is based on a deep triplet network architecture and consists of a variation of the original triplet loss function. Experiments show that the approach can perform liveness detection in real-time outperforming the state-of-the-art on several benchmark datasets.