Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation - ZHANG Xue-qin; LIN Ke-zheng; LI Ao (Journal of Harbin University of Science and Technology)
In order to solve the problem that the face image is not rich in features extracted under complex lighting environments，which leads to a low recognition rate，a local pattern feature extraction and recognition algorithm based on sparse representation is proposed. Firstly，the image is divided into several sub-images and the Dynamic Threshold Central-symmetric Local Binary Pattern ( DTCLBP) algorithm is used to extract features by thresholding the pixels of each sub-block and encoding the results of comparison with the central pixel values into the Central Symmetric Local Binary Pattern ( CSLBP) ; and then second-order features are extracted from the processed image by the former step using the Central Symmetric Local Derivative Pattern ( CSLDP) ; finally，the sparse representation classification algorithm is used to classify and identify the extracted features. The simulation experiments on Extended Yale B，CMU _PIE and AＲ face databases validate the effectiveness of the DTCLBP-CSLDP-SＲC.