  In contrast to comparing faces via single exemplars, matching sets of face
images increases robustness and discrimination performance. Recent image set
matching approaches typically measure similarities between subspaces or
manifolds, while representing faces in a rigid and holistic manner. Such
representations are easily affected by variations in terms of alignment,
illumination, pose and expression. While local feature based representations
are considerably more robust to such variations, they have received little
attention within the image set matching area. We propose a novel image set
matching technique, comprised of three aspects: (i) robust descriptors of face
regions based on local features, partly inspired by the hierarchy in the human
visual system, (ii) use of several subspace and exemplar metrics to compare
corresponding face regions, (iii) jointly learning which regions are the most
discriminative while finding the optimal mixing weights for combining metrics.
Face recognition experiments on LFW, PIE and MOBIO face datasets show that the
proposed algorithm obtains considerably better performance than several recent
state-of-the-art techniques, such as Local Principal Angle and the Kernel
Affine Hull Method.
