Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion
作者：
Highlights：
• This paper is motivated by robust principal component analysis (RPCA).
• We exploit the sparse error component to perform face recognition.
• We define two descriptors (i.e., sparsity and smoothness) to represent the sparse error image.
• We present the weighted based method and ratio based method to classify face images.
• Our method shows good performance on public face databases with illumination and occlusion.
摘要
Highlights•This paper is motivated by robust principal component analysis (RPCA).•We exploit the sparse error component to perform face recognition.•We define two descriptors (i.e., sparsity and smoothness) to represent the sparse error image.•We present the weighted based method and ratio based method to classify face images.•Our method shows good performance on public face databases with illumination and occlusion.
论文关键词：Face recognition,Robust principal component analysis,Sparse error,Illumination,Occlusion
论文评审过程：Received 2 July 2012, Revised 5 May 2013, Accepted 27 June 2013, Available online 16 July 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.06.031
