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Face recognition using local gradient binary count pattern

[+] Author Affiliations
Xiaochao Zhao, Yaping Lin, Bo Ou, Junfeng Yang

Hunan University, College of Computer Science and Electronic Engineering, South Lushan Road, Changsha 410082, China

Zhelun Wu

Tsinghua University, Institute for Interdisciplinary Information Sciences, Beijing 100084, China

J. Electron. Imaging. 24(6), 063003 (Nov 12, 2015). doi:10.1117/1.JEI.24.6.063003
History: Received May 26, 2015; Accepted September 24, 2015
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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.

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Citation

Xiaochao Zhao ; Yaping Lin ; Bo Ou ; Junfeng Yang and Zhelun Wu
"Face recognition using local gradient binary count pattern", J. Electron. Imaging. 24(6), 063003 (Nov 12, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063003


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