Fingerprint Liveness Detection using CNN Features of Random Sample Patches

초록

As a result of the growing use of commercial fingerprint authentication systems in mobile devices, detection of fingerprint spoofing has become increasingly important and widely used. This study proposes a fingerprint liveness?detection method based on convolutional neural network (CNN) features extracted from fingerprint patches. Firstly, fingerprints are segmented, and then data augmentation is performed to increase the size of training data. Secondly, on the augmented fingerprint, locations of patches are determined through normal distributions of segmented areas of the fingerprint image. Finally, a voting strategy is applied on all patches to make a decision of live or fake fingerprints. Experimental results show that the proposed method can be applied to fingerprint liveness detection with a 3.42% average classification error rate on a LivDet2009 Identix sensor dataset.