Cost-sensitive face recognition.
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.
Zhang
Yin
Y
National Key Laboratory for Novel Software Technology, Nanjing University, Mailbox 419, 22 Hankou Road, Nanjing 210093, China. zhangyin@lamda.nju.edu.cn
Zhou
Zhi-Hua
ZH
D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer