  Different from face verification, face identification is much more demanding.
To reach comparable performance, an identifier needs to be roughly N times
better than a verifier. To expect a breakthrough in face identification, we
need a fresh look at the fundamental building blocks of face recognition. In
this paper we focus on the selection of a suitable signal representation and
better matching strategy for face identification. We demonstrate how Gabor
phase could be leveraged to improve the performance of face identification by
using the Block Matching method. Compared to the existing approaches, the
proposed method features much lower algorithmic complexity: face images are
only filtered by a single-scale Gabor filter pair and the matching is performed
between any pairs of face images at hand without involving any training
process. Benchmark evaluations show that the proposed approach is totally
comparable to and even better than state-of-the-art algorithms, which are
typically based on more features extracted from a large set of Gabor faces
and/or rely on heavy training processes.
