Fingerprint Spoof Detector Generalization

We present a style-transfer based wrapper, called Universal Material Generator (UMG), to improve the generalization performance of any fingerprint spoof (presentation attack) detector against spoofs made from materials not seen during training. Specifically, we transfer the style (texture) characteristics between fingerprint images of known materials with the goal of synthesizing fingerprint images corresponding to unknown materials, that may occupy the space between the known materials in the deep feature space. Synthetic live fingerprint images are also added to the training dataset to supervise the CNN to learn generative-noise invariant features which discriminate between lives and spoofs. The proposed approach is shown to improve the generalization performance of two state-of-the-art spoof detectors, namely Fingerprint Spoof Buster and Slim-ResCNN, winner of the LivDet 2017 spoof detection competition. Specifically, the performance is improved from TDR of 75.24% and 73.09% to TDR of 91.78% and 90.63% @ FDR = 0.2% for Spoof Buster and Slim-ResCNN, respectively. These results are based on a large-scale dataset of 5,743 live and 4,912 spoof images fabricated using 12 different materials. In addition to generalization across different spoof materials, the proposed approach is also shown to improve the average cross-sensor spoof detection performance from 67.60% and 64.62% to 80.63% and 77.59%, for Fingerprint Spoof Buster and Slim-ResCNN, respectively, when tested on the LivDet 2017 dataset.