Abstract: Local discriminant embedding attempts to achieve high recognition accuracy, implicitly assuming that all misclassifications lead to the same losses. This assumption, however, may not hold in the practical face recognition systems, because the losses of different mistakes may be different. Motivated by this concern, a new approach called cost-sensitive local discriminant embedding is proposed in this paper. Firstly the feature extraction phase utilizes the cost-sensitive learning technique which helps analysis different misclassifications by constructing the cost matrix. Then we maximize the costs of misclassifying the neighboring points of the different class and minimize the distances of neighboring points of the same class simultaneously. Finally we obtain the optimal orthogonal vectors which help maintain the metric structure by utilizing an iterative algorithm. The extensive experiments on the face database Yale, ORL, AR and Extended Yale B demonstrate the effectiveness of the proposed algorithm.