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 ...

• Type: Journal
• Year: 2010
• Where: CVPR
• Authors: Jiwen Lu, Tan Yap-Peng
• Keywords: Computer Vision, CVPR 2010, Face Recognition, Low Recognition Errors, Subspace Learning-based Face