| Sumario: | In the field the human face is an important entity which plays a crucial role in our daily social interaction, like conveying individual's identity. Face recognition system is also able to recognize the person from a distance without touching or any interaction with the person. Currently, face recognition applications are deployed in social media websites like Facebook, in the entrance of Airports, Railways Stations, Bus Stop, highly secured areas, advertisement, and health care. The purpose of these applications is to minimize criminal activities, fake authentication, tracking addictive gamblers in casinos, whereas Facebook is using face recognition system for automatic tagging purpose. For face recognition purpose, there is a need for large data sets and complex features to uniquely identify the different subjects by manipulating different obstacles like illumination, pose and aging.in this project we proposes a deep unified model for Face Recognition based on Faster Region Convolution Neural Network. Design a group-based face attendance system based on the proposed deep unified model. In our proposed system, we have a number of class rooms of a specific institute in which we setup our face recognition system for making a smart class rooms. Several images from different smart class room Buffys are being sent simultaneously for processing, in order to take the attendance. In order to measure the validity of the proposed algorithm, a web application of a group based face attendance system is developed.
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