Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes

2017 ◽  
Vol 39 (1) ◽  
pp. 156-171 ◽  
Author(s):  
Jian Yang ◽  
Lei Luo ◽  
Jianjun Qian ◽  
Ying Tai ◽  
Fanlong Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Nuclear Norm ◽  
Illumination Changes

Nuclear norm-based matrix regression preserving embedding for face recognition

2018 ◽  
Vol 311 ◽  
pp. 279-290 ◽  
Author(s):  
Yang-Jun Deng ◽  
Heng-Chao Li ◽  
Qi Wang ◽  
Qian Du
Keyword(s):  
Face Recognition ◽  
Nuclear Norm

Robust Face Recognition for Uncontrolled Pose and Illumination Changes

2013 ◽  
Vol 43 (1) ◽  
pp. 149-163 ◽  
Author(s):  
Maria De Marsico ◽  
Michele Nappi ◽  
Daniel Riccio ◽  
Harry Wechsler
Keyword(s):  
Face Recognition ◽  
Illumination Changes ◽  
Robust Face Recognition

Robust Nuclear Norm-Based Matrix Regression With Applications to Robust Face Recognition

2017 ◽  
Vol 26 (5) ◽  
pp. 2286-2295 ◽  
Author(s):  
Jianchun Xie ◽  
Jian Yang ◽  
Jianjun J. Qian ◽  
Ying Tai ◽  
Hengmin M. Zhang
Keyword(s):  
Face Recognition ◽  
Nuclear Norm ◽  
Robust Face Recognition

LBP-like feature based on Gabor wavelets for face recognition

2017 ◽  
Vol 15 (05) ◽  
pp. 1750049 ◽  
Author(s):  
Wen-Juan Li ◽  
Jun Wang ◽  
Zheng-Hai Huang ◽  
Ting Zhang ◽  
Daniel K. Du
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Gabor Transform ◽  
Georgia Tech ◽  
Feature Extraction Method ◽  
Face Representation ◽  
Illumination Changes ◽  
Feature Based ◽  
Robust Feature Extraction ◽  
New Feature

The robust feature extraction method for face representation is an important issue in face recognition. In this paper, we extract a new kind of feature through applying the idea of local binary pattern (LBP) into the resulted sub-images of Gabor transform. The new feature, i.e. Gabor-LBP-Like (GLLBP), together with its extension methods (1) overcome the drawback of losing information after Gabor transform’s down-sampling; (2) are insensitive to noise, compared with the LBP feature extracted from the original face image; and (3) are robust to image variation, especially occlusion and illumination changes when compared with other existing features combined LBP and Gabor transform. To validate the effectiveness of these features, we do experiments on the ORL, FERET, Georgia Tech and LFW facial databases. The numerical results show that GLLBP and its extensions are miraculous features for face recognition.

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