A Survey on Face Recognition Systems

TL;DR

This survey reviews the evolution of face recognition systems, emphasizing deep learning architectures, training methods, and datasets that have significantly improved accuracy in recent years.

Contribution

It provides a comprehensive overview of key face recognition architectures, training techniques, and evaluation datasets, highlighting recent advancements and trends.

Findings

Deep learning has greatly enhanced face recognition accuracy.

Various network architectures and training losses impact system performance.

Multiple datasets are used for evaluating face recognition capabilities.

Abstract

Face Recognition has proven to be one of the most successful technology and has impacted heterogeneous domains. Deep learning has proven to be the most successful at computer vision tasks because of its convolution-based architecture. Since the advent of deep learning, face recognition technology has had a substantial increase in its accuracy. In this paper, some of the most impactful face recognition systems were surveyed. Firstly, the paper gives an overview of a general face recognition system. Secondly, the survey covers various network architectures and training losses that have had a substantial impact. Finally, the paper talks about various databases that are used to evaluate the capabilities of a face recognition system.