Face Recognition Based on Deep Learning: A Comprehensive Review

Abstract

Face recognition technology has undergone transformative changes with the advent of deep learning techniques. This review paper provides a comprehensive examination of the development and current state of face recognition techniques influenced by deep learning. We begin by discussing the fundamental deep learning models that have dramatically enhanced the accuracy and efficiency of face recognition, highlighting pivotal architectures such as convolutional neural networks (CNNs) and autoencoders. Subsequent sections delve into the application of these models in various environments and challenges, such as different lighting conditions, occlusion, and facial expressions. We also address the integration of deep learning with emerging technologies such as 3D facial reconstruction and multimodal biometrics. Furthermore, the review explores the ethical, privacy, and bias concerns inherent in facial recognition systems, focusing on the need for responsible and fair practices in AI. Finally, future directions are suggested, focusing on the need for robust, adaptable, and ethical face recognition systems. This paper aims to provide an important resource for researchers and practitioners in the field of computer vision, providing insight into the technological advances and ongoing challenges in deep learning-based face recognition.