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- W3199631830 abstract "Neural network based-classifiers can combine both statistical and structural information and achieve better performance than the simple minimum distance classifiers. Hence they have been widely used in pattern recognition applications. The performance of neural network based classifier depends on the input feature selection and neural network architectures. Gray-level co-occurrence matrix (GLCM) features are used to build the FRS. The architectures used in this paper are feed forward neural network and single neuron cascaded architecture. For comparison the network complexity has to be maintained constant and hence the neurons used in hidden layers are maintained constant. To reduce computational complexity Tansigmoid function is replaced by Elliot function and the performance is compared. Three different databases namely ORL, YALE, and JAFFE are used for performance comparison." @default.
- W3199631830 created "2021-09-27" @default.
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- W3199631830 date "2016-01-01" @default.
- W3199631830 modified "2023-09-24" @default.
- W3199631830 title "Performance Comparison of Neural Classifiers for Face Recognition System Using GLCM Features" @default.
- W3199631830 doi "https://doi.org/10.2139/ssrn.3535039" @default.
- W3199631830 hasPublicationYear "2016" @default.
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