Adaptive noise dictionary construction via IRRPCA for face recognition. (November 2016)

• Record Type: Journal Article
• Authors: Chen, Yu; Yang, Jian; Luo, Lei; Zhang, Hengmin; Qian, Jianjun; Tai, Ying; Zhang, Jian
• Is Part Of: Pattern recognition. Volume 59(2016:Nov.)
• Page Start: 26
• Page End: 41
• Publication Date: 2016-11
• Subjects: Face recognition -- Robust regression -- Noise dictionary -- Robust principal component analysis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.02.005
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Recently, regression analysis has become a popular method for face recognition. Various robust regression methods have been proposed to handle with different recognition tasks. In this paper, we attempt to achieve this goal by the strategy of adding an adaptive noise dictionary (AND) to the training samples. In contrast to the previous methods, the noise dictionary (ND) is adaptive to different kinds of noise and extracted automatically. To get an effective noise dictionary, the Iteratively Reweighted Robust Principal Component Analysis (IRRPCA) is proposed. A corresponding classifier based on linear regression is presented for recognition. As this adaptive noise dictionary can describe the noise distribution of testing samples, it is robust to various kinds of noise and applicable for recognition tasks with occluded or corrupted images. This method is also extended to deal with misaligned images. Experiments are conducted on AR, Yale B, CMU PIE, CMU Multi-Pie, LFW and Pubfig databases to verify the robustness of our method to variations in occlusion, corruption, illumination, misalignment, etc. Highlights: A novel noise dictionary for regression is developed. The noise dictionary is adaptive to different kinds of noise. Iteratively Reweighted Robust Principal Component Analysis is developed. Augmented Lagrangian Multiplier method is used to solve our model. An extended version is provided to deal with misaligned images.