A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robust face representation and recognition. MBP consists of two parts: one is monogenic magnitude encoded via uniform LBP, and the other is monogenic orientation encoded as quadrant-bit codes. The HMBP is established by concatenating the histograms of MBP of all sub-regions. Compared with the well-known and powerful Gabor filtering based LBP schemes, one clear advantage of HMBP is its lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. The experimental results on the AR and FERET face databases validate that the proposed MBP algorithm has better performance than or comparable performance with state-of-the-art local feature based methods but with significantly lower time and space complexity.
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