Robust, discriminative and comprehensive dictionary learning for face recognition. (September 2018)

• Record Type: Journal Article
• Authors: Lin, Guojun; Yang, Meng; Yang, Jian; Shen, Linlin; Xie, Weicheng
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 341
• Page End: 356
• Publication Date: 2018-09
• Subjects: Dictionary learning -- Face recognition -- Sparse representation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.03.021
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A robust, discriminative and comprehensive dictionary learning (RDCDL) method is proposed. The robust dictionary is learned from sample diversities by extracting real face variations and generating virtual face images. RDCDL learns the dictionary including class-shared dictionary atoms, class-specific dictionary atoms and disturbance dictionary atoms to completely represent the practical data. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discrimination information. RDCDL outperforms those state-of-the-art methods. Abstract: For sparse representation or sparse coding based image classification, the dictionary, which is required to faithfully and robustly represent query images, plays an important role on its success. Learning dictionaries from the training data for sparse coding has shown state-of-the-art results in image classification and face recognition. However, for face recognition, conventional dictionary learning methods cannot well learn a reliable and robust dictionary due to suffering from the small-sample-size problem. The other significant issue is that current dictionary learning do not completely cover the important components of signal representation (e.g., commonality, particularity, and disturbance), which limit their performance. In order to solve the above issues, in this paper, we propose a novel robust, discriminative and comprehensive dictionary learning (RDCDL) method, in which a robust dictionary is learned from comprehensive training sample diversities generated by extracting and generating facial variations. Especially, to completely represent the commonality, particularity and disturbance, class-shared, class-specific and disturbance dictionary atoms are learned to represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which effectively improves the classification capability of the dictionary. The proposed RDCDL method is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.