Issue No. 10 - October (2010 vol. 32)
Yin Zhang , Nanjing University, Nanjing
Zhi-Hua Zhou , Nanjing University, Nanjing
Most traditional face recognition systems attempt to achieve a low recognition error rate, implicitly assuming that the losses of all misclassifications are the same. In this paper, we argue that this is far from a reasonable setting because, in almost all application scenarios of face recognition, different kinds of mistakes will lead to different losses. For example, it would be troublesome if a door locker based on a face recognition system misclassified a family member as a stranger such that she/he was not allowed to enter the house, but it would be a much more serious disaster if a stranger was misclassified as a family member and allowed to enter the house. We propose a framework which formulates the face recognition problem as a multiclass cost-sensitive learning task, and develop two theoretically sound methods for this task. Experimental results demonstrate the effectiveness and efficiency of the proposed methods.
Face recognition, cost-sensitive face recognition, cost-sensitive learning, multiclass cost-sensitive learning.
Z. Zhou and Y. Zhang, "Cost-Sensitive Face Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1758-1769, 2009.