Face recognition uiing trichotomic combination Of SVD, DF-LDA and LPP

One of the challenges the face recognition application is facing today is that of the high dimensionality of multivariate data. In this context, this paper proposes to compare the performance of a triumvirate combination of linear dimensionality reduction techniques namely Singular Value Decomposition (SVD) which maximizes the variance of the training vectors, Direct Fractional Linear Discriminant Analysis (DFLDA) that maximizes the ¿between-class¿ scatter while minimizing the ¿within-class¿ scatter and Locality Preserving Projection (LPP) which preserves the local features those unique from its nearest neighbors. The amalgamation containing different ratios is chosen from the features extracted by the three independent techniques mentioned above. Original Face space is projected onto the manifold of chosen basis. The weights obtained from these projections for the probe set are compared with that of the query image using the mean distance classifier. The proposed method has been tested on YALE dataset and the combination in the ratio 3:2:5 showed significant improvement in the efficiency of recognition, with a calculated accuracy of 92.7% on a test set of 165 images.