  Some face recognition methods are designed to utilize geometric information
extracted from depth sensors to overcome the weaknesses of single-image based
recognition technologies. However, the accurate acquisition of the depth
profile is an expensive and challenging process. Here, we introduce a novel
method that learns to recognize faces from stereo camera systems without the
need to explicitly compute the facial surface or depth map. The raw face stereo
images along with the location in the image from which the face is extracted
allow the proposed CNN to improve the recognition task while avoiding the need
to explicitly handle the geometric structure of the face. This way, we keep the
simplicity and cost efficiency of identity authentication from a single image,
while enjoying the benefits of geometric data without explicitly reconstructing
it. We demonstrate that the suggested method outperforms both existing
single-image and explicit depth based methods on large-scale benchmarks, and
even capable of recognize spoofing attacks. We also provide an ablation study
that shows that the suggested method uses the face locations in the left and
right images to encode informative features that improve the overall
performance.
