Deep Learning Based Representation for Face Recognition

Face Recognition is one of the challenging process due to huge amount of wild datasets. Deep learning has been provided good solution in terms of recognition performance, as day by day this have been dominating the field of biometric. In this paper our goal is to study deep learning based face representation under several different conditions like lower and upper face occlusions, misalignment, different angles of head poses, changing illuminations, flawed facial feature localization using deep learning approaches. For extraction of face representation two different popular models of Deep learning based called Lightened CNN and VGG-Face and have reflected in this paper. As both of this model show that deep learning model is robust to different types of misalignment and can tolerate localizations error of the intraocular distance.