Independent Component Analysis (ICA) is a kind of powerful method for Blind Signal Processing (BSP). It becomes more and more important while using in widely fields, such as telecommunications, audio signal separation, biomedical signal processing, and image processing. Many literatures on ICA were published and lots of algorithms were proposed during the past ten years in a large number of journals and conference proceedings. ICA becomes one of the most exciting new topics both in the fields of signal processing and artificial neuralThe major work of this thesis lies in using a new feature extraction method—Independent Component Analysis. The principle of independent component analysis algorithm is to find the mutual independent underlying components, to remove the higher-order redundant between components and to extract the independent original signals according to the analysis of higher-order statistical relationships among the multidimensional observed data. Features extraction by ICA mainly aims at natural images processing. The specific work as flowering:(1) Arrange basic theory and history of ICA. Meanwhile, some relate probability, statistics and information theory knowledge are introduced.(2) Analysis and explain in detail the independent component analysis theory and algorithm.(3) We extract the independent basic from nature images basing on FastICA. And we also denoising the images containing noises using method of Hyvarinen.(4) In this chapter, we extract PCA and ICA face basic by PCA and ICA respectively. Combine the two coefficient which the test face images basing on PCA and ICA basic, then we use NN and COSIN classifier to recognition. The result express this method is better than only use one method. Then combine ICA and kernel method to recognize the faces.

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