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Robust Matrix Regression for Illumination and Occlusion Tolerant Face Recognition

Jianchun Xie, Jian Yang, Jianjun Qian, Ying Tai
2015 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)  
In this paper, we operate on the two-dimensional image matrix directly, and propose a new face recognition method, namely Robust Matrix Regression (RMR).  ...  with variations of occlusion and illumination.  ...  Naseem et al. presented the linear regression classifier (LRC) [15] and the robust linear regression classifier for face classification (RLRC) [16] .  ... 
doi:10.1109/iccvw.2015.118 dblp:conf/iccvw/XieYQT15 fatcat:m7ib3qf3avb57fzagvbhjts3ay

Robust Low-Rank Regularized Regression for Face Recognition with Occlusion

Jianjun Qian, Jian Yang, Fanglong Zhang, Zhouchen Lin
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
We thus introduce a novel robust low-rank regularized regression (RLR 3 ) method for face recognition with occlusion.  ...  Recently, regression analysis based classification methods are popular for robust face recognition. These methods use a pixel-based error model, which assumes that errors of pixels are independent.  ...  Chen et al. presented a novel low-rank matrix approximation algorithm with structural incoherence for robust face recognition [19] . This paper focuses on face recognition with occlusion.  ... 
doi:10.1109/cvprw.2014.9 dblp:conf/cvpr/QianYZL14 fatcat:qqlow7wyhbcrll44sxjxtqxuti

Robust nuclear norm regularized regression for face recognition with occlusion

Jianjun Qian, Lei Luo, Jian Yang, Fanlong Zhang, Zhouchen Lin
2015 Pattern Recognition  
We thus introduce a novel robust nuclear norm regularized regression (RNR) method for face recognition with occlusion.  ...  Recently, regression analysis based classification methods are popular for robust face recognition. These methods use a pixel-based error model, which assumes that errors of pixels are independent.  ...  robust model to handle face recognition with occlusion.  ... 
doi:10.1016/j.patcog.2015.04.017 fatcat:4z7qi45wmzco7mufio6u3h5qfe

A Robust Method for Partially Occluded Face Recognition

2015 KSII Transactions on Internet and Information Systems  
However, partial face occlusion is one of the most challenging problems in face recognition issue.  ...  Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and  ...  Another important relevant research called Linear regression-based classification (LRC) is proposed for robust face recognition [26] .  ... 
doi:10.3837/tiis.2015.07.019 fatcat:onhhhzpy5jb4zoqd7ysxyfbbw4

Sparsity and Robustness in Face Recognition [article]

John Wright, Arvind Ganesh, Allen Yang, Zihan Zhou, Yi Ma
2011 arXiv   pre-print
This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition.  ...  In this report, we have attempted to clarify some frequently encountered questions about this work and particularly, on the validity of using sparse representation techniques for face recognition.  ...  Linear Models for Face Recognition with Varying Illumination The method of [WYG + 09] is based on low-dimensional linear models for illumination variation in face recognition.  ... 
arXiv:1111.1014v1 fatcat:hmkwb4bgnzesrh7uwpxna6xopu

A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling [article]

Miaohua Zhang, Yongsheng Gao, Jun Zhou
2020 arXiv   pre-print
For example, in robust face recognition, images are often affected by varying types and levels of corruptions, such as random pixel corruptions, block occlusions, or disguises.  ...  In this paper, we address this problem by a unified sparse weight learning and low-rank approximation regression model, which enables the random noises and contiguous occlusions in images to be treated  ...  All the mentioned robust-regression methods had been applied to face recognition and produced promising results.  ... 
arXiv:2005.04619v4 fatcat:5asvj7gnjbdknjnyxudr5arer4

Face Recognition using Multi-Modal Low-Rank Dictionary Learning [article]

Homa Foroughi, Moein Shakeri, Nilanjan Ray, Hong Zhang
2017 arXiv   pre-print
We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation.  ...  Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.  ...  performance in the presence of occlusion, illumination and pose changes, using a few training samples.  ... 
arXiv:1703.04853v1 fatcat:5ysbfhhizngblenfd6pxdisrbi

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

Meng Yang, Lei Zhang, Simon C.K. Shiu, David Zhang
2013 Pattern Recognition  
face recognition (FR).  ...  In this paper, a Gabor feature based robust representation and classification (GRRC) scheme is proposed for robust FR.  ...  of y 0 can be written in terms of for robust face recognition (FR).  ... 
doi:10.1016/j.patcog.2012.06.022 fatcat:qav35knvendbtmm5gqwpqkgkie

Face recognition with contiguous occlusion using markov random fields

Zihan Zhou, Andrew Wagner, Hossein Mobahi, John Wright, Yi Ma
2009 2009 IEEE 12th International Conference on Computer Vision  
Partially occluded faces are common in many applications of face recognition.  ...  Extensive experiments on both laboratory and real-world datasets show that our algorithm tolerates much larger fractions and varieties of occlusion than current state-of-the-art algorithms.  ...  Robustness to occlusion is therefore essential to practical face recognition.  ... 
doi:10.1109/iccv.2009.5459383 dblp:conf/iccv/ZhouWMWM09 fatcat:sa2sa2t4mvfsnirw43xuso6tyu

When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition [article]

Xiang Xu and Pengfei Dou and Ha A. Le and Ioannis A. Kakadiaris
2017 arXiv   pre-print
This paper presents a pose-invariant 3D-aided 2D face recognition system (UR2D) that is robust to pose variations as large as 90? by leveraging deep learning technology.  ...  Extensive experiments are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software  ...  With 2D landmarks and a 3D model, a 3D-2D projection matrix can be estimated. The frontalized image and occlusion map are generated according to the 3D model and projection matrix.  ... 
arXiv:1709.06532v1 fatcat:tspafbyzmvdjbleb2f4vgw2bxu

Recognition of Facial Expressions under Varying Conditions Using Dual-Feature Fusion

Awais Mahmood, Shariq Hussain, Khalid Iqbal, Wail S. Elkilani
2019 Mathematical Problems in Engineering  
The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise.  ...  Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise.  ...  extracted features are sensitive to the change in illumination, occlusion, and noise. at means a slight change in illumination, occlusion, and noise may influence the recognition accuracy rate.  ... 
doi:10.1155/2019/9185481 fatcat:avzao2uncng65b6f6lwqyqy6y4

Fast Approximate L_infty Minimization: Speeding Up Robust Regression [article]

Fumin Shen, Chunhua Shen, Rhys Hill, Anton van den Hengel, Zhenmin Tang
2013 arXiv   pre-print
Minimization of the L_∞ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression.  ...  This method, termed Fast L_∞ Minimization, allows robust regression to be applied to a class of problems which were previously inaccessible.  ...  The M-estimator method is utilized in [11] for face recognition and achieved high accuracy even when illumination change and pixel corruption were present.  ... 
arXiv:1304.1250v1 fatcat:fck5c6zao5dxfeq3ixnpz3wtly

Efficient 3D Face Recognition with Gabor Patched Spectral Regression

Yue Ming, Qiuqi Ruan, Xueqiao Wang
2012 Computing and informatics  
In this paper, we utilize a novel framework for 3D face recognition, called 3D Gabor Patched Spectral Regression (3D GPSR), which can overcome some of the continuing challenges encountered with 2D or 3D  ...  To solve these problems, we introduce a Patched Spectral Regression strategy, which can make good use of the robustness and efficiency of accurate patched discriminant low-dimension features and minimize  ...  Due to the limitations of face recognition, 2D face recognition still encounters many unsolved difficulties to develop a robust face recognition system, including poses, expressions, illumination, aging  ... 
dblp:journals/cai/MingRW12 fatcat:744q5omwyfhfjehryrifwda7iu

Linear Regression for Face Recognition

Imran Naseem, Roberto Togneri, Mohammed Bennamoun
2010 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression.  ...  The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.  ...  Contiguous Occlusion The problem of face identification in the presence of contiguous occlusion is arguably one of the most challenging paradigms in the context of robust face recognition.  ... 
doi:10.1109/tpami.2010.128 pmid:20603520 fatcat:hzi3li3lrzfezldlbfsb34wxrq

Competitive Representation Based Classification Using Facial Noise Detection

Tao Liu, Cong Li, Ying Liu, Chao Li
2016 International Journal of Advanced Computer Science and Applications  
Linear representation based face recognition is hotly studied in recent years.  ...  We compare the proposed method with others on AR, Extended Yale B, ORL, FERET, and LFW databases and the experimental results show the good performance of our method.  ...  And their later work uses Huber estimator to achieve more robust regression against different levels illumination changes.  ... 
doi:10.14569/ijacsa.2016.070314 fatcat:uzgcoibxtjgbfc5xz4gnb3nddi
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