Deep Learning For Face Recognition: A Critical Analysis

TL;DR

This paper critically analyzes modern face recognition methods, highlighting their advancements, limitations, and challenges such as computational cost and invariance issues, to guide future research and practical applications.

Contribution

It provides a comprehensive comparison of deep and shallow face recognition solutions, identifying current limitations and areas for future development.

Findings

Deep neural networks improve accuracy but are computationally intensive.

Invariance issues like occlusion and pose affect recognition performance.

The survey highlights the need for more efficient and robust face recognition methods.

Abstract

Face recognition is a rapidly developing and widely applied aspect of biometric technologies. Its applications are broad, ranging from law enforcement to consumer applications, and industry efficiency and monitoring solutions. The recent advent of affordable, powerful GPUs and the creation of huge face databases has drawn research focus primarily on the development of increasingly deep neural networks designed for all aspects of face recognition tasks, ranging from detection and preprocessing to feature representation and classification in verification and identification solutions. However, despite these improvements, real-time, accurate face recognition is still a challenge, primarily due to the high computational cost associated with the use of Deep Convolutions Neural Networks (DCNN), and the need to balance accuracy requirements with time and resource constraints. Other significant issues affecting face recognition relate to occlusion, illumination and pose invariance, which causes a notable decline in accuracy in both traditional handcrafted solutions and deep neural networks. This survey will provide a critical analysis and comparison of modern state of the art methodologies, their benefits, and their limitations. It provides a comprehensive coverage of both deep and shallow solutions, as they stand today, and highlight areas requiring future development and improvement. This review is aimed at facilitating research into novel approaches, and further development of current methodologies by scientists and engineers, whilst imparting an informative and analytical perspective on currently available solutions to end users in industry, government and consumer contexts.