The human brain is trained in such a way that it can automatically and instantly recognize multiple faces. But when it comes to the computer, it becomes very difficult to do all the challenging tasks on the level of the human brain. The face recognition is an integral part of biometrics. In biometrics, basic traits of human are matched to the existing data and facial features are extracted and implemented through algorithms. Numerous algorithms and techniques have been developed for improving the performance of face recognition. Deep learning is making crucial advances in solving problems that have restricted the best attempts of the artificial intelligence community for many years. Among one of the deep learning, approaches are Convolutional Neural networks (CNN).CNN is a kind of artificial neural networks that employ convolution methodology to extract the features from the input data to increase the number of features. The general structure of the face recognition process is of three stages. It starts with a pre-processing stage: color space conversion and resizing of images, continues with the extraction of facial features, and afterward extracted feature set is classified. In our system, Softmax Classifier is to realize the final stage that is classification on the basis of the facial features extracted from CNN