Unknown face presentation attack detection via localized learning of multiple kernels

Abstract

The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown attacks. While earlier studies have revealed the benefits of ensemble methods, and in particular, a multiple kernel learning (MKL) approach to the problem, one limitation of such techniques is that they treat the entire observation space similarly and ignore any variability and local structure inherent to the data. This work studies this aspect of face presentation attack detection with regards to one-class multiple kernel learning to benefit from the intrinsic local structure in bona fide samples to adaptively weight each representation in the composite kernel. More concretely, drawing on the one-class Fisher null formalism, we formulate a convex localised multiple kernel learning algorithm by regularising the collection of local kernel weights via a joint matrix-norm constraint and infer locally adaptive kernel 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 its advantages over some other options. An assessment of the proposed approach on general object image datasets illustrates its efficacy for anomaly and novelty detection while the results of the experiments on face PAD datasets verify its potential in detecting unknown/unseen face presentation attacks.