Face recognition is one of the most efficient applications of computer authentication and pattern recognition. Therefore it attracts significant attention of researchers. In the past decades, many feature extraction algorithms have been proposed. In this paper Gabor features and Zernike moment were used to extract features from human face images for recognition application. This paper is a study for new constructive training algorithm for Multi Layer Perceptron (MLP) which is applied to face recognition application. An incremental training procedure was employed where the training patterns are learned incrementally. This algorithm started with a single training pattern and a single hidden-layer using one neuron. During neural network training, the hidden neuron is increased when the Mean Square Error (MSE) of the Training Data (TD) is not reduced or the algorithm gets stuck in a local minimum. Input patterns are trained incrementally (one by one) until all patterns of TD are selected and trained. Face recognition system structure based on a MLP neural network was constructed and was tested for face recognition. The proposed approach was tested on the UMIST database. Experimental results indicate that we can obtain an optimal architecture of neural network classifier (with the least possible number of hidden neuron) using our present constructive algorithm, and prove the effectiveness of the proposed method compared to the MLP architecture with back-propagation algorithm.
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.
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