Face recognition system using multi layer feed Forward Neural Networks and Principal Component Analysis with variable learning rate

In this paper we have proposed a new way to achieve the optimum learning rate that can reduce the learning time of the multi layer feed forward neural network. The effect of optimum numbers of inner iterations and numbers of hidden nodes on learning time and recognition rate has been shown. The Principal Component Analysis and Multilayer Feed Forward Neural Network are applied in face recognition system for feature extraction and recognition respectively. The paper shows that the recognition rate and training time are dependent on numbers on hidden nodes. In this approach we have used variable learning rate and demonstrated its superiority over constant learning rate. We have used ORL database for all the experiments.