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Gender recognition plays an important role for a wide range of application in the field of Human Computer Interaction. In this paper, we propose a gender recognition system based on 2D Discrete Cosine Transform and Neural Networks. In particular, a discriminability criterion is used to select the DCT coefficients that make up the biometric template. Experimental results show how the proposed approach leads to a significant enhancement of recognition performances.
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