Unseen face presentation attack detection using sparse multiple kernel fisher null-space

We address the face presentation attack detection problem in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not available in the training stage. To this aim, we pose the unseen face presentation attack detection (PAD) problem as the one-class kernel Fisher null-space regression and present a new face PAD approach that only uses bona fide (genuine) samples for training. Drawing on the proposed kernel Fisher null-space face PAD method and motivated by the availability of multiple information sources, next, we propose a multiple kernel fusion anomaly detection approach to combine the complementary information provided by different views of the problem for improved detection performance. And the last but not the least, we introduce a sparse variant of our multiple kernel Fisher null-space face PAD approach to improve inference speed at the operational phase without compromising much on the detection performance. The results of an experimental evaluation on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method outperforms other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (that benefit from presentation attack data for training) despite using only bona fide samples in the training stage.