Robust face recognition based on dynamic rank representation

作者：

Highlights：

• Dynamic rank estimation can extract optimal subspace which can recover the face images from corruption.

• Improves the efficiency of discriminative feature selection.

• Needs lower dimensions training samples but gains a higher recognition rate.

• Achieves competitive results with accuracy, robustness and efficiency.

摘要

•Dynamic rank estimation can extract optimal subspace which can recover the face images from corruption.•Improves the efficiency of discriminative feature selection.•Needs lower dimensions training samples but gains a higher recognition rate.•Achieves competitive results with accuracy, robustness and efficiency.

论文关键词：Face recognition,Low-rank representation,Dynamic subspace,Discriminative component,Occlusion

论文评审过程：Received 11 September 2015, Accepted 10 May 2016, Available online 20 May 2016, Version of Record 31 May 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.014