MLP neural network using constructive training algorithm: application to face recognition and facial expression recognition
| Autor: | Hayet Boughrara, Chokri Ben Amar, Mohamed Chtourou |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Facial expression
General Computer Science Mean squared error Biometrics Artificial neural network business.industry Computer science Speech recognition Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Facial recognition system Gabor filter Computer Science::Computer Vision and Pattern Recognition Multilayer perceptron 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Algorithm |
| Zdroj: | International Journal of Intelligent Systems Technologies and Applications. 16:53 |
| ISSN: | 1740-8873 1740-8865 |
| Popis: | This paper presents a constructive training algorithm applied to face recognition and facial expression recognition. The multi layer perceptron MLP neural network is formed by a single hidden layer using a predefined number of neurons and a small number of training patterns. During the learning, the hidden neuron number is incremented when the mean square error MSE on the training data TD is not reaches a predefined value. Input patterns are learned incrementally until all patterns of TD are presented. The proposed algorithm allows to find synthesis parameters as the number of patterns corresponding for subsets of each class to be presented initially in the training step, the initial number of hidden neurons, the iterations number as well as the MSE value. The feature extraction stage is based on the perceived facial images and the Gabor filter. Compared to the literature review and the fixed MLP architecture, experimental results demonstrate the efficiency of the proposed approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|