  The paper studies face spoofing, a.k.a. presentation attack detection (PAD)
in the demanding scenarios of unknown types of attack. While earlier studies
have revealed the benefits of ensemble methods, and in particular, a multiple
kernel learning approach to the problem, one limitation of such techniques is
that they typically treat the entire observation space similarly and ignore any
variability and local structure inherent to the data. This work studies this
aspect of the face presentation attack detection problem in relation to
multiple kernel learning in a one-class setting to benefit from intrinsic local
structure in bona fide face samples. More concretely, inspired by the success
of the one-class Fisher null formalism, we formulate a convex localised
multiple kernel learning algorithm by imposing a joint matrix-norm constraint
on the collection of local kernel weights and infer locally adaptive weights
for zero-shot one-class unseen attack detection.
  We present a theoretical study of the proposed localised MKL algorithm using
Rademacher complexities to characterise its generalisation capability and
demonstrate the advantages of the proposed technique over some other options.
An assessment of the proposed approach on general object image datasets
illustrates its efficacy for abnormality and novelty detection while the
results of the experiments on face PAD datasets verifies its potential in
detecting unknown/unseen face presentation attacks.
