中文摘要：

传统的局部保持降维方法追求最低的识别错误率,即假设每一类的错分代价都是相同的.这个假设在真实的人脸识别应用中往往是不成立的.人脸识别是一个多类的代价敏感和类不平衡问题.例如,在人脸识别的门禁系统中,将入侵者错分成合法者的损失往往高于将合法者错分成入侵者的损失.因此,每一类的错分代价是不同的.另外,如果任一类合法者的样本数少于入侵者的样本数,该类合法者和入侵者就是类别不平衡的.为此,将错分代价融入到局部保持的降维模型中,提出了一种错分代价最小化的局部保持降维方法.同时,采用加权策略平衡了各类样本对投影方向的贡献.在人脸数据集AR,PIE,Extended Yale B 上的实验结果表明了该算法的有效性.

英文摘要：

Conventional locality preserving projection aims to minimize recognition error rate, implicitly assuming the losses of different misclassifications are the same. However, this may not hold in many real world face recognition applications. Face recognition is a multiclass cost sensitive and class imbalance task. For example, it will be troublesome if a gallery person is misclassified as an impostor, but it will be much more costly if an impostor is misclassified as a gallery person. Consequently, different kinds of mistakes will lead to different losses. Moreover, the examples of gallery person from any class are fewer than examples of the impostor, which is referred to as class imbalance. For that, this paper integrates the misclassification costs into an objective function of the locality preserving projection and proposes a cost sensitive locality preserving projection that minimizes the misclassification costs. Simultaneously, a weight strategy is used to balance the contribution of each class to the projection. Experimental results on AR, PIE, Extended Yale B datasets demonstrate the superiority of the proposed method.