Face recognition is one of the most important and challenging issues in computer vision and image processing. About half a century ago, since the first face recognition system was introduced, facial recognition has become one of the most important issues in industry and academia. In recent years, with the developing of computers throughput and developments of a new generation of hierarchical learning algorithms called deep learning, much attention has been devoted to solving learning problems by deep learning algorithms. Deep neural networks perform feature learning instead of feature extraction which by this strategy they are much useful for image processing and computer vision problems. Deep neural network through feature learning perform data representation well and have gained many successes in learning and complex problems, many studies have been done on the application of deep neural networks to face recognition and many successes has been achieved. In this study we examine the neural network based methods used for face recognition such as multilayer perceptrons, restricted Boltzmann machine and auto encoders. Most of our study devoted to convolutional neural network as one of the most successful deep learning algorithms. At the end we have examined the results of the encountered methods on ORL, AR, YALE, FERET datasets and show deep neural network has gained high recognition rate in comparing with benchmark methods. تفاصيل المقالة