An Evaluation of the Invariance Properties of a Biologically-Inspired System for Unconstrained Face Recognition

A key challenge in building face recognition systems — biologically-inspired or otherwise — is evaluating performance. While much of face recognition research has traditionally used posed photographs for evaluation, recent efforts have emerged to build more naturalistic, unconstrained test sets by collecting large numbers of face images from the internet (e.g. the “Labeled Faces in the Wild”(LFW) test set [1]). While such efforts represent a large step forward in the direction of realism, the nature of posed photographs from the internet arguably represents an incomplete sampling of the range of variation in view, lighting, etc. found in the real world. Here, we evaluate a family of large-scale biologically-inspired vision algorithms that has previously proven to perform well on a variety of object and face recognition test sets [2], and show that members of this family perform at a level of performance that is comparable with current state-of-the-art approaches on the LFW challenge. As a counterpoint to internet-photo based approaches, we use synthetic (rendered) face images where the amount of view variation is controllable and known by design. We show that while there is gross agreement between the LFW benchmark and synthetic benchmarks, the synthetic benchmarks reveal a substantially greater degree of tolerance to view variation than is apparent from the LFW benchmark in models containing deeper hierarchies. Furthermore, such an approach yields important insights into which axes of variation are most challenging. These results suggest that parametric synthetic benchmarks can play an important role in guiding the progress of biologically-inspired vision systems.