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Hybrid approach has a special status among Face Recognition Systems as theycombine different recognition approaches in an either serial or parallel toovercome the shortcomings of individual methods. This paper explores the areaof Hybrid Face Recognition using score based strategy as a combiner/fusionprocess. In proposed approach, the recognition system operates in two modes:training and classification. Training mode involves normalization of the faceimages (training set), extracting appropriate features using PrincipleComponent Analysis (PCA) and Independent Component Analysis (ICA). Theextracted features are then trained in parallel using Back-propagation neuralnetworks (BPNNs) to partition the feature space in to different face classes.In classification mode, the trained PCA BPNN and ICA BPNN are fed with new faceimage(s). The score based strategy which works as a combiner is applied to theresults of both PCA BPNN and ICA BPNN to classify given new face image(s)according to face classes obtained during the training mode. The proposedapproach has been tested on ORL and other face databases; the experimentedresults show that the proposed system has higher accuracy than face recognitionsystems using single feature extractor.

Hybrid Approach to Face Recognition System using Principle component and Independent component with score based fusion process