代价敏感的监督流形学习人脸识别方法

Face Recognition Method Based on Cost-Sensitive Supervised Manifold Learning

基于子空间学习的人脸识别均假设所有错误识别会导致一样的损失。在人脸识别应用中,不同的错误识别造成的损失则不同。提出一种代价敏感的监督流形学习人脸识别方法,该方法采用一个代价矩阵来指定不同的误分类代价,并将其容纳到局部保持投影(Locality Preserving Projections,LPP)算法中,获得相应的代价敏感局部保持投影(Cos-Sen LPP),以实现人脸识别整体损失最小化。在3个人脸数据库上的实验结果表明,与现有的子空间学习方法相比,Cos-Sen LPP方法花费了最少的整体代价。

Existing subspace learning-based face recognition methods assume the same loss from all misclassifications. In the real-world face recognition applications, however, different misclassifications can lead to different losses. Motivated by this concern, a cost-sensitive supervised manifold learning approach for face recognition was proposed. The proposed approach incorporated a cost matrix to specify the different costs associated with misclassifications of subjects, into locality preserving projection algorithm, which devised the corresponding cost-sensitive methods, namely, cost-sensitive locality preserving projections（Cos-Sen LPP）, to achieve a minimal overall loss. Three face databases were put into the experiments and experimental results show that Cos-Sen LPP method can achieve minimal cost than existing subspace learning-based face recognition methods.