KPCA与SVM共同用于人脸识别 SVM提高了分类效果 KPCA是一种借鉴SVM中核函数的一种较好的特征提取方法-KPCA and SVM for face recognition SVM together to improve the classification results from KPCA is a kernel function in SVM a better feature extraction method

In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. With the weights of the trained neural networks there are created kernel window

它是混合核函数方面的，对于分类的研究有很好的帮助的作用。而且是关于人脸识别的。-It is a hybrid kernel function aspects of the study of the role of classification good help. And about face recognition.

研究基于步态的身份识别技术,并在步态特征提取和身份识 别等方面做了一些简单的尝试和探索。本文提出了一种基于核函数的Fisher判别分析(KFDA)进行步态识别.-In training process,we use kernel-based Fisher Discrimination Analysis(KFDA) method to train the input sample vectors.The method has been used in face recognition an