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20122026
most citedRFN-Nest: An end-to-end residual fusion network for infrared and visible images

1.1k citations

Showing 2019Show all

11 papers · 1 filter

cs.CV2019

A sparsity augmented probabilistic collaborative representation based classification method

Xiao-Yun Cai, He-Feng Yin

In order to enhance the performance of image recognition, a sparsity augmented probabilistic collaborative representation based classification (SA-ProCRC) method is presented. The…

cs.CV2019

2DR1-PCA and 2DL1-PCA: two variant 2DPCA algorithms based on none L2 norm

Xing Liu, Xiao-Jun Wu, Zi-Qi Li

In this paper, two novel methods: 2DR1-PCA and 2DL1-PCA are proposed for face recognition. Compared to the traditional 2DPCA algorithm, 2DR1-PCA and 2DL1-PCA are based on the R1 no…

cs.CV2019

Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering

Kai Xu, Xiao-Jun Wu, Wen-Bo Hu

Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric c…

cs.CV20193 cited

Collaborative representation-based robust face recognition by discriminative low-rank representation

Wen Zhao, Xiao-Jun Wu, He-Feng Yin +1

We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional sub…

cs.CV2019

Low-rank representations with incoherent dictionary for face recognition

Pei Xie, He-Feng Yin, Xiao-Jun Wu

Face recognition remains a hot topic in computer vision, and it is challenging to tackle the problem that both the training and testing images are corrupted. In this paper, we prop…

cs.CV20191 cited

Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning

He-Feng Yin, Xiao-Jun Wu, Josef Kittler

Face recognition has been widely studied due to its importance in smart cities applications. However, the case when both training and test images are corrupted is not well solved.…