Gabor features have been widely employed in solving face recognition problems in controlled scenarios. To construct discriminative face features from the complex Gabor space, the amplitude information is commonly preferred, while the other one — the phase — is not well utilized due to its spatial shift sensitivity. In this paper, we address the problem of face recognition in controlled scenarios. Our focus is on the selection of a suitable signal representation and the development of a better strategy for face feature construction. We demonstrate that through our Block Matching scheme Gabor phase information is powerful enough to improve the performance of face identification. Compared to state of the art Gabor filtering based approaches, the proposed algorithm features much lower algorithmic complexity. This is mainly due to our Block Matching enables the employment of high definition Gabor phase. Thus, a single-scale Gabor frequency band is sufficient for discrimination. Furthermore, learning process is not involved in the facial feature construction, which avoids the risk of building a database-dependent algorithm. Benchmark evaluations show that the proposed learning-free algorith outperforms state-of-the-art Gabor approaches and is even comparable to Deep Learning solutions.
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