代价敏感的局部判别嵌入人脸识别算法

摘要

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 con-cern, 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 misclas-sifications 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 simultane-ously. 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.%局部判别嵌入算法寻求最高的正确识别率时假设所有的错误分类具有相同的错分代价,然而这个假设在现实的人脸识别系统中往往是不成立的,因为不同的错误分类将会导致不同的错分代价。为此,提出一种代价敏感的局部判别嵌入算法。首先通过构造代价矩阵将代价敏感理念融入到特征提取阶段,以提高算法判别不同错误分类的能力；然后最大化异类近邻样本点之间的错分代价,同时最小化同类近邻样本点之间的距离；最后利用迭代算法求得最佳的正交投影向量,以更好地维持数据的度量架构。在Yale, ORL, AR和Extended Yale B人脸数据库上的实验结果表明,文中算法是有效的。