Attendance Compilation by Facial Recognition Methods of Image Processing: A Review

Abstract

Abstract: In recent years, researchers have continued to refine and improve deep learning-based approaches to image processing, as well as exploring new areas such as generative adversarial networks (GANs) and reinforcement learning. This paper provides a comprehensive survey of deep learning-based methods for face recognition, including CNN-based models, auto encoder models, and hybrid models. These papers demonstrate the effectiveness of deep convolutional neural networks (CNNs) in face detection, recognition, and attendance compilation, achieving state-of-the-art accuracy on several benchmark datasets, including LFW, YouTube Faces, and YTF datasets. The Efficient Net model is a family of CNNs that achieves state of the art accuracy on multiple image recognition benchmarks while being significantly smaller and faster than previous models. The Arc Face loss function is used for facial landmark detection and gender classification in facial images.