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Over the past years, various face recognition technique have been developed and applied to authentication and surveillance. Among them, the most popular face recognition method is based on the principal component analysis (PCA) and linear discriminant analysis (LDA) method. PCA captures the sub-space to maximize scatter of data and LDA captures the sub-space to maximize between scatter of data and minimize with -in scatter of data.Since PCA (1) and LDA (2, 3) method, however, project fea-tures onto the linear subspaces, their computational time de-pends on the dimension of features and the number of linear subspaces. Therefore, in the large-scale high-dimensional fea-ture database, the PCA and LDA based system spend too much time on their projection and distance computation.In this paper, we propose an efficient face recognition meth-od using cascade feature filter and subspace projection. The main difference between feature filter and subspace projection are described on the Fig. 1. Since the feature filter approach selects only the relevant features, the computational time of distance measure between images can be reduced, but it loses the infor-mation of entire structure. On the other hand, the subspace projection approach maintains the information of entire struc-

An Efficient Face Recognition using Feature Filter and Subspace Projection Method