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Journal articles
Face recognition based on perceived facial images and multilayer perceptron neural network using constructive training algorithm
Abstract : This study presents a modified constructive training algorithm for multilayer perceptron (MLP) which is applied to face recognition problem. An incremental training procedure has been employed where the training patterns are learned incrementally. This algorithm starts with a small number of training patterns and a single hidden-layer using an initial number of neurons. During the training, the hidden neurons number is increased when the mean square error (MSE) threshold of the training data (TD) is not reduced to a predefined value. Input patterns are trained incrementally until all patterns of TD are learned. The aim of this algorithm is to determine the adequate initial number of hidden neurons, the suitable number of training patterns in the subsets of each class and the number of iterations during the training step as well as the MSE threshold value. The proposed algorithm is applied in the classification stage in face recognition system. For the feature extraction stage, this paper proposes to use a biological vision-based facial description, namely perceived facial images, applied to extract features from human face images. Gabor features and Zernike moment have been used in order to determine the best feature extractor. The proposed approach is tested on the Cohn-Kanade Facial Expression Database. Experimental results indicate that a good architecture of neural network classifier can be obtained. The effectiveness of the proposed method compared with the fixed
MLP architecture has been proved.
https://hal.archives-ouvertes.fr/hal-01301128
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Monday, April 11, 2016 - 4:30:47 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:45 PM
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Monday, April 11, 2016 - 4:30:47 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:45 PM
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