Discriminative multi-scale sparse coding for single-sample face recognition with occlusion. (June 2017)

• Record Type: Journal Article
• Authors: Yu, Yu-Feng; Dai, Dao-Qing; Ren, Chuan-Xian; Huang, Ke-Kun
• Is Part Of: Pattern recognition. Volume 66(2017:Jun.)
• Page Start: 302
• Page End: 312
• Publication Date: 2017-06
• Subjects: Face recognition -- Multi-scale -- Intra-class variant -- Sparse coding -- Outlier pixels; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.01.021
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: The single sample per person (SSPP) face recognition is a major problem and it is also an important challenge for practical face recognition systems due to the lack of sample data information. To solve SSPP problem, some existing methods have been proposed to overcome the effect of variances to test samples in illumination, expression and pose. However, they are not robust when the test samples are with different kinds of occlusions. In this paper, we propose a discriminative multi-scale sparse coding (DMSC) model to address this problem. We model the possible occlusion variations via the learned dictionary from the subjects not of interest. Together with the single training sample per person, most of types of occlusion variations can be effectively tackled. In order to detect and disregard outlier pixels due to occlusion, we develop a multi-scale error measurements strategy, which produces sparse, robust and highly discriminative coding. Extensive experiments on the benchmark databases show that our DMSC is more robust and has higher breakdown point in dealing with the SSPP problem for face recognition with occlusion as compared to the related state-of-the-art methods. Abstract : Highlights: An intra-class variant dictionary is learned based on PCA. Propose multi-scale error measurement strategy to improve sparsity and robustness. An optimization algorithm is proposed to solve multi-scale sparse coding model.