Cost-Sensitive Subspace Learning for Face Recognition

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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 ...
Jiwen Lu, Tan Yap-Peng
Added 12 Jan 2011
Updated 12 Jan 2011
Type Journal
Year 2010
Where CVPR
Authors Jiwen Lu, Tan Yap-Peng
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