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Many face recognition algorithms/systems have been developed in the last decade and excellent performances are also reported when there is sufficient number of representative training samples. In many real-life applications, only one training sample is available. Under this situation, the performance of existing algorithms will be degraded dramatically or the formulation is incorrect, which in turn, the algorithm cannot be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples, but also consider the face detection localization error while training. After that, we employ a sub-space LDA method, which is tailor-made for small number of training samples, for the local feature projection to maximize the discrimination power. Finally, combining the contributions of each local feature draws the recognition decision. FERET database is used for evaluating the proposed method and results are encouraging.