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Proceedings Paper

Fusing global and local features for face verification
Author(s): Ji Zhou; Biahua Xiao; Chunheng Wang; Xinyuan Cai; Xue Chen
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Paper Abstract

In the literature of neurophysiology and computer vision, global and local features have both been demonstrated to be complementary for robust face recognition and verification. In this paper, we propose an approach for face verification by fusing global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from ten different component patches by a new image representation method named Histogram of Local Phase Quantization Ordinal Measures (HOLPQOM). Experimental results on the Labeled Face in Wild (LFW) benchmark show the robustness of the proposed local descriptor, compared with other often-used descriptors.

Paper Details

Date Published: 19 July 2013
PDF: 5 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887814 (19 July 2013); doi: 10.1117/12.2030875
Show Author Affiliations
Ji Zhou, Institute of Automation (China)
Biahua Xiao, Institute of Automation (China)
Chunheng Wang, Institute of Automation (China)
Xinyuan Cai, Institute of Automation (China)
Xue Chen, Institute of Automation (China)


Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

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