稀疏表示的局部模式特征提取与识别  

Local Pattern Feature Extraction and Recognition Based on Sparse Representation

作　　者：张雪琴 林克正[1] 李骜[1] ZHANG Xue-qin;LIN Ke-zheng;LI Ao(School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]哈尔滨理工大学计算机科学与技术学院,哈尔滨150080

出　　处：《哈尔滨理工大学学报》2021年第4期102-108,共7页Journal of Harbin University of Science and Technology

基　　金：国家自然科学基金(61501147);黑龙江省自然科学基金(YQ2019F011);黑龙江省青年创新人才项目(UNPYSCT-2018203);黑龙江省教育中心科技研究项目(11551087);黑龙江省基本科研业务专项项目(LGYC2018JQ013).

摘　　要：针对人脸图像在复杂光照环境下提取的特征不够丰富,导致识别率低的问题,提出一种稀疏表示的局部模式特征提取与识别算法。首先将图像进行分块,依次对每个子区域的像素进行阈值化处理,并将其与中心像素值比较的结果编码到中心对称局部二值模式算法中来实现特征提取;然后在此基础上采用中心对称局部方向模式算法提取二阶特征,得到最终的纹理特征;最后结合稀疏表示分类器对提取的特征进行分类识别。在Extended Yale B、CMU_PIE、AR数据集上的仿真实验验证了所提方法的有效性。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.

关 键 词：中心对称局部二值模式 中心对称局部方向模式 特征提取 稀疏表示 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]