Facial Feature Extraction Based on Robust PCA and Histogram

摘要：

　　Inspired by recently-proposed robust principal component analysis (RPCA),in this paper we present a feature extraction method for robust face recognition in the presence of random pixel corruption and occlusion.Unlike most work focusing on the low-rank structure recovered by RPCA,we consider that the sparse error component contains more discriminating power which is essential to face recognition.In order to illustrate the intensity distribution of the sparse error component,a histogram-based sparsity measure is introduced for feature extraction.Compared with the related state-of-the-art methods,experimental results on Extended Yale B database verify the advancement of the proposed method for partially corrupted and occluded face images.

关键词： Face recognition Robust PCA Sparse Histogram

作者: Xiao Luan Weisheng Li

作者单位: Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 296-302

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）