Face Recognition system identifies a person by comparing numerous face images accumulated in database records. Face Recognition is simply matching human beings by their faces. This technology has augmented in the field of security and law enforcement to track down criminals and terrorists. In our method, we use deep convolution neural network (deep CNN) and Euclidean distance for extracting the feature from face images. Euclidean Distance used for counting distance between images. We have used FEI dataset for face recognition. This paper gives brief information about face recognition techniques like OpenFace, EigenFace, LBPH, Fisher-Face, and Deep CNN. This paper contains basic information about CNN architecture like AlexNet, GoogleNet, VGGNet, ResNet, SENet, etc. that are used to recognize any type of pose variation in the image. CNN architecture plays an important role to achieve the best accuracy. This paper also focuses on some publicly available datasets: CelebFace (2014), Facebook (2014), Google (2015), MegaFace (2016), MS-Celeb-1M (2018).

Face Recognition, Face detection, Tensorflow, CNN architectures, Datasets, deep CNN

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