Description

In our paper, we present a cost-sensitive semi-supervised discriminant analysis method for face recognition. In previous methods of dimensionality reduction, they aim to seek low-dimensional feature representations to achieve low classification errors. In many real-worlds 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 misrecognized 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 misrecognized as a gallery person and allowed to enter the room. Motivated by this concern, we propose in this paper a new method to learn a discriminative feature sub-space by making use of both labeled and unlabeled samples and exploring different cost information of all the training samples simultaneously.