Cost-Sensitive Subspace Analysis and Extensions for Face Recognition
Conventional subspace-based face recognition methods seek low-dimensional feature subspaces to achieve high classification accuracy and assume the same loss from different types of misclassification. This assumption, however, may not hold in many practical face recognition systems as different types of misclassification could lead to different losses. Motivated by this concern, this paper proposes a cost-sensitive subspace analysis approach for face recognition. Our approach uses a cost matrix specifying different costs corresponding to different types of misclassifications, into two popular and widely used discriminative subspace analysis methods and devises the cost-sensitive linear discriminant analysis (CSLDA) and cost-sensitive marginal fisher analysis (CSMFA) methods, to achieve a minimum overall recognition loss by performing recognition in these learned low-dimensional subspaces. To better exploit the complementary information from multiple features for improved face recognition, we further propose a multiview cost-sensitive subspace analysis approach by seeking a common feature subspace to fuse multiple face features to improve the recognition performance. Extensive experimental results demonstrate the effectiveness of our proposed methods.