FACE-RECOGNITION TO AUTHENTICATE STUDENTS

Abstract

Within the framework of this project, a face recognition system is being de- veloped, which will be used in educational institutions to identify students who are taking exams. To achieve both high quality and fast results, I focused on deep learning approaches to face and object detection and
recognition. This research is mainly aimed at providing neural networks and other models with enough data to achieve the desired results. Starting with the basics of neural networks, in which I described and explored a neuron, the smallest unit of deep learning, I brought my research to the point where I could detect a person’s face or an object in a photograph. This research began with the development of neural networks and went on to train them on both the CPU and GPU. In a technique called matrix backpropagation, multiple GPUs were used in conjunction with the CUDA core and the cuBLAS library. Face identification was performed using a pretrained Facenet model combined with deep convolutional neural networks. Numerous ap- proaches to deep learning have been developed from the study of neural networks and their application to face recognition.