Research of face recognition methods based on subspace analysis

Face recognition is an important problem of pattern recognition and machine learning. Among many approaches to the problem of face recognition, subspace analysis gives the most promising results, and becomes one of the most popular methods. This paper researches subspace analysis methods, introduces the basic theory of linear subspace such as PCA、LDA、ICA 、FastICA etc. and non-linear subspace such as KPCA etc. and their application in face recognition ,including some new research fruits concretely . In addition ,ORL database and YALE B database are used to experiment basic subspace methods. The experiment results indicate that FastICA method is more powerful than other subspace methods for face recognition. Finally, the advantage and disadvantage of these methods are discussed by the experiment results.