Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold  

作　　者：韩胜 席诗琼 耿卫东 

机构地区：[1]Key Laboratory of Photo-Electronics Thin Film Devices and Technique of Tianjin, Key Laboratory of Opto-electronic Information Science and Technology, Ministry of Education, Institute of Photo-electronics Thin Film Devices andTechnique, Nankai University, Tianjin 300071, China

出　　处：《Optoelectronics Letters》2017年第6期444-447,共4页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61401237)

摘　　要：In order to solve the problem of low recognition rate of traditional feature extraction operators under low-resolution images, a novel algorithm of expression recognition is proposed, named central oblique average center-symmetric local binary pattern(CS-LBP) with adaptive threshold(ATCS-LBP). Firstly, the features of face images can be extracted by the proposed operator after pretreatment. Secondly, the obtained feature image is divided into blocks. Thirdly, the histogram of each block is computed independently and all histograms can be connected serially to create a final feature vector. Finally, expression classification is achieved by using support vector machine(SVM) classifier. Experimental results on Japanese female facial expression(JAFFE) database show that the proposed algorithm can achieve a recognition rate of 81.9% when the resolution is as low as 16×16, which is much better than that of the traditional feature extraction operators.

关 键 词：operators HISTOGRAM OBLIQUE PRETREATMENT classifier FACIAL symmetric Japanese BLOCKS pixel 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]