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《Transducer and Microsystem Technologies》 2019-05
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Face recognition algorithm based on block LBP fusion feature and SVM

ZHANG Dunfeng;GAO Ninghua;WANG Heng;FENG Xinghua;HUO Jianwen;ZHANG Jing;School of Information Engineering,Southwest University of Science and Technology;  
Aiming at the problem that the traditional local binary pattern( LBP) feature extraction method does not perform well under the condition of changing of light and facial expression,and the feature extracted by a single method cannot express the feature information of the entire face from multiple angles,an approach of face recognition based on block local binary pattern( LBP) fusion feature and the support vector machine( SVM) is proposed. For each face image,it is divided into several blocks,and the LBP features are extracted from each block,then pixel means in different blocks are fused with LBP features. The fused feature vector by all the blocks including LBP features and average pixel values are used to represent the whole face. Finally,the SVM is introduced and used as classifier to classify the above features. Experiments are carried out on YALE,ORL and self-built face database. It turns out that the recognition accuracy can respectively reach 95. 15 %,99. 75 % and96. 25 %. Comparative experiments show that this method is superior to the traditional methods such as C4. 5 decision tree and random forest.
【Fund】: 四川省科技计划资助项目(2019JDRC0141)
【CateGory Index】: TP391.41;TP181
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