MLP neural network using constructive training algorithm: application to face recognition and facial expression recognition
Hayet Boughrara, Mohamed Saber Chtourou, Chokri Ben Amar · International Journal of Intelligent Systems Technologies and Applications · 2017
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.