  Hybrid approach has a special status among Face Recognition Systems as they
combine different recognition approaches in an either serial or parallel to
overcome the shortcomings of individual methods. This paper explores the area
of Hybrid Face Recognition using score based strategy as a combiner/fusion
process. In proposed approach, the recognition system operates in two modes:
training and classification. Training mode involves normalization of the face
images (training set), extracting appropriate features using Principle
Component Analysis (PCA) and Independent Component Analysis (ICA). The
extracted features are then trained in parallel using Back-propagation neural
networks (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 face
image(s). The score based strategy which works as a combiner is applied to the
results of both PCA BPNN and ICA BPNN to classify given new face image(s)
according to face classes obtained during the training mode. The proposed
approach has been tested on ORL and other face databases; the experimented
results show that the proposed system has higher accuracy than face recognition
systems using single feature extractor.
