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A High Precision Feature Based on LBP and Gabor Theory for Face Recognition
Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China
* Author to whom correspondence should be addressed.
Received: 7 February 2013; in revised form: 19 March 2013 / Accepted: 19 March 2013 / Published: 3 April 2013
Abstract: How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.
Keywords: face recognition; new feature; robust; Gabor; LBP; PCA; LDA
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MDPI and ACS Style
Xia, W.; Yin, S.; Ouyang, P. A High Precision Feature Based on LBP and Gabor Theory for Face Recognition. Sensors 2013, 13, 4499-4513.
Xia W, Yin S, Ouyang P. A High Precision Feature Based on LBP and Gabor Theory for Face Recognition. Sensors. 2013; 13(4):4499-4513.
Xia, Wei; Yin, Shouyi; Ouyang, Peng. 2013. "A High Precision Feature Based on LBP and Gabor Theory for Face Recognition." Sensors 13, no. 4: 4499-4513.