Advancements in Face Recognition Using Deep Learning Techniques: A Comprehensive Review

Abstract

This paper presents a comprehensive overview of recent developments in face recognition using deep learning approaches. We discuss the evolution of deep learning architectures for face recognition, including variations of CNNs such as Siamese networks, triplet loss networks, and attention mechanisms. Furthermore, we explore the challenges and strategies associated with training deep learning models for face recognition tasks, including data augmentation, transfer learning, and domain adaptation. Additionally, we highlight recent advancements in face recognition applications, including face verification, identification, and emotion recognition. Finally, we discuss future directions and emerging trends in face recognition research, such as privacy-preserving techniques, multimodal fusion, and the integration of deep learning with other biometric modalities. This survey provides valuable insights into the state-of-the-art techniques and potential avenues for advancing face recognition using deep learning in various real-world scenarios.