A Real Time Face Attendance Using Deep Learning

Abstract: One area of biometry technologies which is expanding quickly and being used extensively is face attendance in real time.Its uses are finding solutions for the industry, institutions, and law departments.The development of deep neural networks i ntended for all sets offace identification tasks, from recognition and preprocessing to image presentation and classification in ve rifying and finding answers, has become the primary focus of research due to the recent introduction of powerful, reasonably pri ced GPUs and the growth of massive face databases.The high processing expense of using Deep Convolutions Neural Networks ( DCNN) and the need to strike a compromise between accuracy requirements and time and resource restrictions are the main rea sons why realtime, exact image detection remains difficult despite recent advancements.Other important problems with face reco gnition include posture invariance, light, and occlusion, which significantly reduce accuracy in both deep neural networks and c onventional handmade methods.It highlights areas that need further growth and improvement and offers a thorough analysis of both deep and shallow solutions .Inaddition to providing end users in business, government, and consumer with an insightful an d critical viewpoint on currently available solutions, this review aims to support scientists' and engineers' research into new appr oaches of existing methodologies.