A Survey on Face Recognition Using Convolutional Neural Network

Face recognition is an important concept, which has generally considered in the course of recent decades. Generally image location can be considered as an extraordinary sort of item recognition in PC vision. In this paper, we explore one of the vital and very effective systems for conventional item discovery using convolutional neural network (CNN) method, that is, a gentle classifier development for resolving the substance identification problem. That in recognizing the face of images as the problem is very difficult one, and so far no quality results are been obtained. Usually, this problem splits into distinctive sub-issues, to make it simpler to work predominantly identification of face of a picture pursued by the face acknowledgment itself. There are several tasks to perform in between such as partial image face detection or extracting more features from them. Many years there are numerous calculations and systems have been utilized such as eigenfaces or active shape model, principal component analysis (PCA), K-nearest neighbour (KNN), and local binary pattern histograms (LBPH), but accurate results have not been identified. However because of the drawbacks of previously mentioned techniques in my study, I want to use CNN in deep learning to obtain best results.