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Hybrid N-Feature Face Recognition System


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  • Product Description

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.

Product Specifications
SKU :COC17359
AuthorT. M. Kodinariya
Number of Pages216
Publishing Year2012-07-08T00:00:00.000
Edition1 st
Book TypeComputing & information technology
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-07-26 00:00:00
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