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Face Recognition with Patterns of Oriented Edge Magnitudes

  • Ngoc-Son Vu
  • Alice Caplier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

This paper addresses the question of computationally inexpensive yet discriminative and robust feature sets for real-world face recognition. The proposed descriptor named Patterns of Oriented Edge Magnitudes (POEM) has desirable properties: POEM (1) is an oriented, spatial multi-resolution descriptor capturing rich information about the original image; (2) is a multi-scale self-similarity based structure that results in robustness to exterior variations; and (3) is of low complexity and is therefore practical for real-time applications. Briefly speaking, for every pixel, the POEM feature is built by applying a self-similarity based structure on oriented magnitudes, calculated by accumulating a local histogram of gradient orientations over all pixels of image cells, centered on the considered pixel. The robustness and discriminative power of the POEM descriptor is evaluated for face recognition on both constrained (FERET) and unconstrained (LFW) datasets. Experimental results show that our algorithm achieves better performance than the state-of-the-art representations. More impressively, the computational cost of extracting the POEM descriptor is so low that it runs around 20 times faster than just the first step of the methods based upon Gabor filters. Moreover, its data storage requirements are 13 and 27 times smaller than those of the LGBP (Local Gabor Binary Patterns) and HGPP (Histogram of Gabor Phase Patterns) descriptors respectively.

Keywords

Face Recognition Recognition Rate Face Image Local Binary Pattern Gabor Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ngoc-Son Vu
    • 1
    • 2
  • Alice Caplier
    • 2
  1. 1.Vesalis Sarl, Clermont FerrandFrance
  2. 2.Gipsa-lab, Grenoble INPFrance

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