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Journal of Electronic Imaging

Face recognition using local gradient binary count pattern
Author(s): Xiaochao Zhao; Yaping Lin; Bo Ou; Junfeng Yang; Zhelun Wu
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Paper Abstract

A local feature descriptor, the local gradient binary count pattern (LGBCP), is proposed for face recognition. Unlike some current methods that extract features directly from a face image in the spatial domain, LGBCP encodes the local gradient information of the face’s texture in an effective way and provides a more discriminative code than other methods. We compute the gradient information of a face image through convolutions with compass masks. The gradient information is encoded using the local binary count operator. We divide a face into several subregions and extract the distribution of the LGBCP codes from each subregion. Then all the histograms are concatenated into a vector, which is used for face description. For recognition, the chi-square statistic is used to measure the similarity of different feature vectors. Besides directly calculating the similarity of two feature vectors, we provide a weighted matching scheme in which different weights are assigned to different subregions. The nearest-neighborhood classifier is exploited for classification. Experiments are conducted on the FERET, CAS-PEAL, and AR face databases. LGBCP achieves 96.15% on the Fb set of FERET. For CAS-PEAL, LGBCP gets 96.97%, 98.91%, and 90.89% on the aging, distance, and expression sets, respectively.

Paper Details

Date Published: 12 November 2015
PDF: 14 pages
J. Electron. Imag. 24(6) 063003 doi: 10.1117/1.JEI.24.6.063003
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Xiaochao Zhao, Hunan Univ. (China)
Yaping Lin, Hunan Univ. (China)
Bo Ou, Hunan Univ. (China)
Junfeng Yang, Hunan University (China)
Zhelun Wu, Tsinghua Univ. (China)

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