Hybrid N-Feature Face Recognition System - Kodinariya, T. M.
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The work explores the area of Hybrid Face Recognition using neural networks as classifier. 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…mehr

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Produktbeschreibung
The work explores the area of Hybrid Face Recognition using neural networks as classifier. 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 face databases; the experimented results show that the proposed system has higher accuracy than face recognition systems using single feature extractor.
Autorenporträt
Prof. T. M. Kodinariya received Bachelor¿s degree in Computer Engg. from North Gujarat University. She completed her Master of Computer engineering in 2005 from DDU-Nadiad. Her areas of research are Image Processing, Data Mining, compiler Design and Theory of computation. She published many papers in National and International Conference/Journal.