Cost-sensitive dictionary learning for face recognition. (December 2016)

• Record Type: Journal Article
• Authors: Zhang, Guoqing; Sun, Huaijiang; Ji, Zexuan; Yuan, Yun-Hao; Sun, Quansen
• Is Part Of: Pattern recognition. Volume 60(2016:Dec.)
• Page Start: 613
• Page End: 629
• Publication Date: 2016-12
• Subjects: Cost sensitive -- Sparse representation -- Dictionary learning -- Face recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.06.012
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: As one of the most popular research topics, sparse representation and dictionary learning technique has received an increasing amount of interest in recent years. Sparse representation based classification (SRC) has been shown to be an effective method and produce impressive performance on face recognition. SRC directly used the entire set of training samples as the dictionary for sparse coding. Recent research has shown that learning a dictionary from the training samples instead of using a predefined one can produce state-of-the-art results. However, all of these dictionary learning methods are designed to achieve low classification errors and implicitly assumes that the losses of all misclassification are the same. In many real-world face recognition applications, this assumption may not hold as different misclassifications could lead to different losses. Motivated by this concern, in this paper we propose a cost-sensitive dictionary learning algorithm for SRC, by which the designed dictionary is able to produce cost-sensitive sparse coding, resulting in improved classification performance in such scenarios. Our method considers the cost information during the sparse coding stages. Specifically, we introduce a new "cost" penalizing matrix and enforce the cost-sensitive requirement throughout the learning process. The optimal solution is efficiently obtained following the alternative optimization method. Experimental results demonstrate the effectiveness of the proposed method. Highlights: A cost-sensitive dictionary learning algorithm for SRC is proposed. Introduce a new "cost" penalizing matrix during the sparse coding stages. Enforce cost-sensitive requirement throughout the learning process. The learned dictionary is able to produce cost-sensitive sparse coding. Our method can achieve a minimum overall recognition loss.