Kernel Nearest Feature Line Embedding for Use in Face Recognition

• Main Authors: Liu Yu Shu, 劉玉樹
• Other Authors: 陳映濃
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/91221985660135938461

碩士 === 國立中央大學 === 資訊工程學系在職專班 === 102 === In traditional discriminant analysis, PCA is usually applied for data preprocessing. However, PCA may bring damage to the topology of original data and hence decrease the discriminability. To remedy this problem, the Kernel methods are adopted to transform the data set from original space to feature space for enhancing the discriminability in this study. In the kernel space, the PCA is then applied to extract the principal component data and remove the noises. After the PCA process, the NFLE algorithm is applied for discriminant analysis. In the experiments, Linear Kernel+NFLE , Guassian Kernel+NFLE, Polynomial Kernel+NFLE algorithms are implemented for face recognition. In our work,the CMU face database is used for evaluating the performance of the proposed methods in different training samples and dimensions. Experimental results reveal that the recognition rate of the proposed kernel based method is lower than NFLE under few training samples. When the number of training samples increases, the proposed kernel based method outperforms NFLE.