Deep face recognition for dim images

作者：

Highlights：

• The proposed framework effectively enhances the performance of deep face recognition for low illumination images.

• Our feature restoration network achieves computational efficiency at the cost of only a few more parameters and FLOPs compared to the original feature extraction model. In addition, the training of this network does not require a massive dataset.

• Our feature restoration network and embedding refinement module can be integrated with most existing deep face recognition models to create an end-to-end face recognition pipeline.

摘要

•The proposed framework effectively enhances the performance of deep face recognition for low illumination images.•Our feature restoration network achieves computational efficiency at the cost of only a few more parameters and FLOPs compared to the original feature extraction model. In addition, the training of this network does not require a massive dataset.•Our feature restoration network and embedding refinement module can be integrated with most existing deep face recognition models to create an end-to-end face recognition pipeline.

论文关键词：Face recognition,Dim image,Rank-1 identification accuracy,Two-branch network,Convolutional neural network

论文评审过程：Received 28 June 2021, Revised 30 December 2021, Accepted 7 February 2022, Available online 8 February 2022, Version of Record 12 February 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108580