Cost-sensitive subspace learning for face recognition

Conventional subspace learning-based face recognition aims to attain low recognition errors and assumes same loss from all misclassifications. In many real-world face recognition applications, however, this assumption may not hold as different misclassifications could lead to different losses. For example, it may cause inconvenience to a gallery person who is mis-recognized as an impostor and not allowed to enter the room by a face recognition-based door-locker, but it could result in a serious loss or damage if an impostor is mis-recognized as a gallery person and allowed to enter the room. Motivated by this concern, we propose in this paper a cost-sensitive subspace learning approach for face recognition. Our approach incorporates a cost matrix, which specifies the different costs associated with misclassifications of subjects, into three popular subspace learning algorithms and devise the corresponding cost-sensitive methods, namely, cost-sensitive principal component analysis (CSPCA), cost-sensitive linear discriminant analysis (CSLDA), and cost-sensitive locality preserving projections (CSLPP), to achieve a minimum overall recognition loss by performing recognition in the low-dimensional subspaces derived. Experimental results are presented to demonstrate the efficacy of the proposed approach.