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• W2214673872 abstract "The paper proposes a face recognition system using Zernike moments ZM and feed forward neural network as a classifier. Magnitudes of the ZM, which are invariant to rotation, are used as feature vectors for efficient representation of the images. The experiment was conducted on the ORL and Texas 3D Face Recognition Database which has both colour and range images. The recognition performance with measures like overall recognition accuracy, false acceptance rate, false rejection rate and true rejection rate was evaluated with multilayer perceptron neural network, radial basis function neural network and probabilistic neural network for variable lengths of the feature vector using confusion matrix. The simulation results indicates that the invariant ZM with neural network classifier was successful in recognising the images constrained to different variations and illumination conditions. The overall classification accuracy of 99.7% with MLPNN and 99.6% with MLPNN was achieved with range images and grey images from Texas 3D Face Recognition Database, respectively. Furthermore, 99.5% accuracy with RBFNN was achieved from ORL database." @default.
• W2214673872 created "2016-06-24" @default.
• W2214673872 creator A5042251884 @default.
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• W2214673872 date "2015-01-01" @default.
• W2214673872 modified "2023-09-25" @default.
• W2214673872 title "Invariant face recognition using Zernike moments combined with feed forward neural network" @default.
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• W2214673872 doi "https://doi.org/10.1504/ijbm.2015.071950" @default.
• W2214673872 hasPublicationYear "2015" @default.
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