  The paper addresses face presentation attack detection in the challenging
conditions of an unseen attack scenario where the system is exposed to novel
presentation attacks that were not present in the training step. For this
purpose, a pure one-class face presentation attack detection approach based on
kernel regression is developed which only utilises bona fide (genuine) samples
for training. In the context of the proposed approach, a number of innovations,
including multiple kernel fusion, client-specific modelling, sparse
regularisation and probabilistic modelling of score distributions are
introduced to improve the efficacy of the method. The results of experimental
evaluations conducted on the OULU-NPU, Replay-Mobile, Replay-Attack and
MSU-MFSD datasets illustrate that the proposed method compares very favourably
with other methods operating in an unseen attack detection scenario while
achieving very competitive performance to multi-class methods (benefiting from
presentation attack data for training) despite using only bona fide samples for
training.
