| Summary: | 碩士 === 國防大學中正理工學院 === 電子工程研究所 === 91 === Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been successfully used for pattern recognition. PCA is computed from the global covariance matrix of the full set of image data, the obtained basis vectors are global representations that are not suitable for the recognition of non-aligned faces. Relatively, ICA basis vectors are more spatially local than PCA basis vectors and give better face representation. This paper addresses the evaluation results of noise immunity for PCA and ICA in face recognition. The recognition performance of PCA and ICA are compared and analyzed when the test images are contaminated by noises. Also, different methods for eigenvector selection and similarity measures are evaluated. ICA has achieved better recognition performance in noise immunity than PCA, as shown in the experimental results. Therefore, ICA is applicable for face recognition under noise environment.
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