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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15378

Title: Dynamic Weighted discrimination Power Analysis in DCT Domain for Face and Palmprint Recognition
Authors: L. Leng
Muhammad Khurram Khan
Khaled Alghathbar
Keywords: DPA, DCTCs
Issue Date: 2010
Abstract: Discrimination power analysis (DPA) is a statistical analysis combining discrimination concept with discrete cosine transform coefficients (DCTCs) properties. Unfortunately there is not a uniform and effective criterion to optimize the shape and size of premasking window on which the effect of DPA excessively relies. Proper premasking is an auxiliary process to select the feature coefficients that have more discrimination power (DP). Dynamic weighted DPA (DWDPA) is proposed in this paper to enhance the DP of the selected DCTCs without premasking window, in other words, it does not need to optimize the shape and size of premasking window. The DCTCs are adaptively selected according to their discrimination power values (DPVs). More DCTCs with higher DP are preserved. The selected coefficients are normalized and dynamic weighted according to their DPVs. Normalization assures that the DCTCs with large absolute value don't destroy the DP of the other DCTCs that have less absolute value but high DPVs. Dynamic weighting gives larger weights to the DCTCs with larger DPVs which optimizes and enhances the recognition performance. The experimental results on ORL, Yale and PolyU databases show that DWDPA outperforms DPA obviously.
URI: http://hdl.handle.net/123456789/15378
Appears in Collections:College of Computer and Information Sciences

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