Optimal feature extraction and face recognition based on kernel machine-based one-parameter multiple discriminant analysis
基于核的多元区别分析算法的特征抽取及其在人脸识别中的应用

A new algorithm, namely kernel machine-based one-parameter multiple discriminant analysis (K1PMDA), to extract optimal discriminant features was proposed and applied to face recognition. There are two problems in linear face recognition: One is that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The other is the small sample size (S3) problem. This problem occurs when the number of training samples is smaller than the dimensionality of feature vectors, which results in a singular within-class scatter matrix. For the former, kernel technique can be used to extract nonlinear feature, and for the latter, a disturbed parameter was introduced to overcome S3 problem. Three databases, namely ORL, Yale Group B, and UMIST were selected for evaluation. The results are encouraging.