Abstract
Two Dimensional Principal Component Analysis(2DPCA) extracts the global feature of human face,but the local feature is very important to face recognition.In this paper,adaptively weighted 2DPCA based on local feature was proposed.Firstly,the face image was separated into three independent sub-blocks according to the local features.Secondly,2DPCA was applied to the sub-blocks independently.Then the method can adaptively compute the contributions made by each sub-block and endow them to the classification in order to improve the recognition performance.The experiments on the ORL and Yale face databases demonstrate the proposed method's effectiveness and feasibility.
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