An antinoise sparse representation method for robust face recognition via joint l1 and l2 regularization

作者：

Highlights：

• L1 or L2 regularization based representation is not antinoise enough.

• An antinoise sparse representation via joint L1 and L2 is proposed.

• The rationale of the objective function for fusion is analyzed.

• Recognition of noisy samples is evaluated as true positive.

• The Anti-L1L2 outperforms some state-of-art sparse algorithms.

摘要

•L1 or L2 regularization based representation is not antinoise enough.•An antinoise sparse representation via joint L1 and L2 is proposed.•The rationale of the objective function for fusion is analyzed.•Recognition of noisy samples is evaluated as true positive.•The Anti-L1L2 outperforms some state-of-art sparse algorithms.

论文关键词：Regularization,Sparse representation,Collaborative representation,Antinoise,Face recognition

论文评审过程：Received 27 November 2016, Revised 31 March 2017, Accepted 1 April 2017, Available online 1 April 2017, Version of Record 5 April 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.001