Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11056
Title: Monogenic binary pattern (MBP) : a novel feature extraction and representation model for face recognition
Authors: Yang, M
Zhang, L 
Zhang, L
Zhang, D 
Issue Date: 2010
Source: Proceedings - International Conference on Pattern Recognition, 2010, p. 2680-2683 How to cite?
Abstract: 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 subregions. 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.
Description: 2010 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, 23-26 August 2010
URI: http://hdl.handle.net/10397/11056
ISBN: 9780769541099
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.657
Appears in Collections:Conference Paper

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