基于Cost-Sensitive主成分分析的人脸识别

目前现有的人脸识别算法寻求最高的正确识别率，且假设所有的错误分类具有相同的错分代价，但此假设在现实的人脸识别系统中往往不成立。为此，提出一种基于代价敏感（Cost-Sensitive）主成分分析的人脸识别方法，该方法在主成分分析理论中引入一个代价敏感函数，将不同错误识别所造成的损失进行分类划分，以确定不同的损失代价，实现更精确的人脸识别。在AR、FERET和UMIST人脸数据集上的实验结果表明，与经典的基于子空间的人脸识别方法相比，提出的方法以最少的代价达到了较高的k最近邻分类识别精度。

Face recognition based on Cost-Sensitive principal component analysis

Existing face recognition algorithms aim 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. Motivated by this concern, a new face recognition approach based on Cost-Sensitive Principal Component Analysis （Cost-Sensitive PCA） is proposed in this paper. It incorporates a cost sensitive function into Principal Component Analysis theory and determines the different loss cost by differentiating losses caused by different error recognition, which achieves more accurate face recognition. The experimental results on AR, FERET and UMIST face databases show that the proposed method achieves higher k nearest neighbor classification recognition accuracy with the least cost compared with the classical subspace-based face recognition methods.