Effective Feature Selection for Face Recognition Based on Correspondence Analysis and Trained Artificial Neural Network

this paper presents a face recognition method based on correspondence analysis (CA) and trained artificial neural network. In this algorithm, features are extracted using CA, then these features are fed to Multi layer Perceptron (MLP)network for classification and finally, after training the network, effective features are selected with UTA algorithm. The obtained experimental results indicate high average accuracy (98%) and the minimum run time of the algorithm as well.