| Summary: | 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.
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