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12 November 2015 Face recognition using local gradient binary count pattern
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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.
© 2015 SPIE and IS&T 1017-9909/2015/$25.00 © 2015 SPIE and IS&T
Xiaochao Zhao, Yaping Lin, Bo Ou, Junfeng Yang, and Zhelun Wu "Face recognition using local gradient binary count pattern," Journal of Electronic Imaging 24(6), 063003 (12 November 2015).
Published: 12 November 2015

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