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This paper presents a cost-sensitive semi-supervised discriminant analysis method for face recognition. While a number of semi-supervised dimensionality reduction algorithms have been proposed in the literature and successfully applied to face recognition in recent years, most of them aim to seek low-dimensional feature representations to achieve low classification errors and assume the same loss from all misclassifications in the feature representation/extraction phase. 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 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 subspace by making use of both labeled and unlabeled samples and exploring different cost information of all the training samples simultaneously. Experimental results are presented to demonstrate the efficacy of the proposed method.