【摘要】 传统的PCA和LDA算法受限于“小样本问题”,且对象素的高阶相关性不敏感。文章将核函数方法与规范化LDA相结合,将原图像空间通过非线性映射变换到高维特征空间,并借助于“核技巧”在新的空间中应用鉴别分析方法。通过对ORL人脸库的大量实验研究表明,该文方法在特征提取方面明显优于PCA、KPCA、LDA等其他传统的人脸识别方法,在简化分类器的同时,也可以获得高识别率。更多还原

【Abstract】 Traditional methods,such as PCA(Principle Component Analysis) and LDA(Linear Discriminant Analysis),not only suffer from the so-called "Small Sample Size"(SSS) problem,but also are insensitive to the high order relations of image pixels. In this paper,we propose kernel machine based regularized discriminant analyzing method,which projects the image space to the high dimensional feature subspace through some non-linear transformation,and then performs discriminant analysis using the "kernel-skills" on the new feature subspace.Extensive experiments on ORL database indicate the method proposed outperforms the traditional PCA,KPCA,LDA methods on feature extraction.It can also simplify the classifier design,meanwhile,accomplish high recognition rate.更多还原