Enhanced nuclear norm based matrix regression for occluded face recognition

作者：

Highlights：

• Our model imposes a nuclear norm constraint on the reconstructed image suppressing the noise in training set by giving the contaminated training sample a small weight.

• Enforcing the reconstructed image to be low rank, our model endows small coefficients to the samples from the incorrect class and emphasize samples from the correct class.

• Our model avoids misclassification caused by collaborative representation consequently improves the discriminative ability of our model.

• Experiments show that the proposed model is more robust than NMR and other relate model in face recognition tasks with contiguous occlusions and illumination changes.

摘要

•Our model imposes a nuclear norm constraint on the reconstructed image suppressing the noise in training set by giving the contaminated training sample a small weight.•Enforcing the reconstructed image to be low rank, our model endows small coefficients to the samples from the incorrect class and emphasize samples from the correct class.•Our model avoids misclassification caused by collaborative representation consequently improves the discriminative ability of our model.•Experiments show that the proposed model is more robust than NMR and other relate model in face recognition tasks with contiguous occlusions and illumination changes.

论文关键词：Face recognition,Occluded image,Nuclear norm,Low-Rank

论文评审过程：Received 23 July 2020, Revised 7 January 2022, Accepted 9 February 2022, Available online 13 February 2022, Version of Record 22 February 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108585