Face Recognition

The face images in real datasets are usually corrupted by shadows, specularities and occlusions, which cannot be handled by classical PCA. Therefore, many approaches based on RPCA were proposed to process face images. As illustrated in Figure 1, the local defects in face images could be removed as the sparse component, while the correct description of the person’s face could be obtained from the low-rank component. This procedure can improve the characterization of faces and boost the performance of recognition algorithms.

Publication

J. Wright, A. Yang, A. Ganesh, S. Sastry, Y. Ma,"Robust Face Recognition via Sparse Representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009.

E. Chitrahadi, C. Basaruddin, "Evaluation of image enhancement quality measure in Robust PCA for Image Specularities Removal", IEEE International Conference on Application of Information and Communication Technologies, AICT 2011, pages 1-5; 2011.

X. Luan, B. Fang, L. Liu, W. Yang, J. Qian, "Extracting sparse error of robust PCA for face recognition in the presence of illumination and occlusion", Pattern Recognition, Volume 47, No. 2, pages 495–508, 2014.

W. Zhao, X. Wu, H. Yin, “Collaborative Representation-Based Robust Face Recognition by Discriminative Low-Rank Representation”, IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, pages 21-27, 2015.

H. Lia, C. Suen, “Robust face recognition based on dynamic rank representation”, Pattern Recognition, Volume 60, pages 13–24, December 2016.

C. Chen, B. Zhang, A. Bue, V. Murino, “Manifold Constrained Low Rank Decomposition”, International Workshop on RSL-CV in conjunction with ICCV 2017, October 2017.

N. Xue, J. Deng, S. Cheng,Y. Panagakis, S. Zafeiriou, "Side Information for Face Completion: a Robust PCA Approach", Preprint, 2018.

S. Moschoglou, E. Ververas, Y. Panagakis, M. Nicolaou, S. Zafeiriou, “Multi-Attribute Robust Component Analysis for Facial UV Maps”, IEEE Journal of Selected Topics in Signal Processing, December 2018.

L. Wang, B. Wang, Z. Zhang, Q. Ye, L. Fu, G. Liu, M. Wang, "Robust auto-weighted projective low-rank and sparse recovery for visual representation", Neural Networks, Volume 117, 201-215, 2019.

C. Zhang, H. Likassa, P. Liang, J. Guo, "New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data", Hindawi Modelling and Simulation in Engineering, November 2021.