MLP neural network using modified constructive training algorithm: Application to face recognition

This paper focuses on the study of modified constructive training algorithm for Multi Layer Perceptron “MLP” which is applied to face recognition applications. In general, constructive learning begins with a minimal structure, and increases the network by adding hidden neurons until a satisfactory solution is found. The contribution of this paper is to increment the output neurons simultaneously with incrementing the input patterns. In fact, the proposed algorithm started with a small number of output neurons and a single hidden-layer using an initial number of neurons. During neural network training, the hidden neurons number is increased while the Mean Square Error “MSE” threshold of the Training Data “TD” is not reduced to a predefined parameter. The output neurons number is increased as the input patterns are incrementally trained until all patterns of Training Data “TD” are presented and learned. The proposed algorithm is applied in the classification stage in face recognition system. For the feature extraction stage, a biological vision-based facial description, namely Perceived Facial Images “PFI” is applied to extract features from human face images. The proposed approach is tested on the Cohn-Kanade Facial Expression Database. Compared to the fixed “MLP” architecture and the constructive training algorithm, experimental results clearly demonstrate the efficiency of the proposed algorithm.