Cost-sensitive dictionary learning for face recognition

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.