人脸识别中基于核的子空间鉴别分析

尽管基于Fisher准则的线性鉴别分析被公认为特征抽取的有效方法之一，并被成功地用于人脸识别，但是由于光照变化、人脸表情和姿势变化，实际上的人脸图像分布是十分复杂的，因此，抽取非线性鉴别特征显得十分必要。为了能利用非线性鉴别特征进行人脸识别，提出了一种基于核的子空间鉴别分析方法。该方法首先利用核函数技术将原始样本隐式地映射到高维（甚至无穷维）特征空间；然后在高维特征空间里，利用再生核理论来建立基于广义Fisher准则的两个等价模型；最后利用正交补空间方法求得最优鉴别矢量来进行人脸识别。在ORL和NUST603两个人脸数据库上，对该方法进行了鉴别性能实验，得到了识别率分别为94％和99．58％的实验结果，这表明该方法与核组合方法的识别结果相当，且明显优于KPCA和Kernel fisherfaces方法的识别结果。

Subspaces Discriminant Analysis Based Kernel Trick for Human Face Recognition

Linear discriminant analysis based on Fisher criterion is one of effective methods for feature extraction, and it was successfully utilized for face recognition. But face image data distribution in practice is highly complex because of illumination, facial expression and pose variations. So it is necessary to extract nonlinear features for face recognition. A novel method called subspace discriminant analysis based on kernel trick is presented in this paper. In the new approach, the kernel trick is used firstly to project the original samples into an implicit space called feature space by nonlinear kernel mapping, then two equivalent models based on generalized Fisher criterion have established by the Theory of Reproducing Kernel in the feature space, and the optimal discriminant vectors are solved finally by using the technique of orthogonal complementary space. The proposed algorithm was tested and evaluated on the ORL face database and the NUST603 face database, which can reach recognition rate shuch as 94% and 99.58% , respectively. The experimental results show that the novel method outperforms both KPCA in8,9] and Kernel fisherfaces in 13] and is comparable with the method in 14 ] in terms of correct recognition rate.