Face Recognition: From Traditional to Deep Learning Methods

TL;DR

This paper reviews the evolution of face recognition from traditional hand-crafted feature methods to modern deep learning approaches, highlighting recent advances and current trends in the field.

Contribution

It provides a comprehensive literature review comparing traditional face recognition techniques with recent deep learning methods, emphasizing the transition and advancements.

Findings

Deep learning methods outperform traditional techniques in accuracy.

Traditional methods are still relevant in resource-constrained scenarios.

The field has shifted towards large datasets and neural networks.

Abstract

Starting in the seventies, face recognition has become one of the most researched topics in computer vision and biometrics. Traditional methods based on hand-crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. In this paper we provide a comprehensive and up-to-date literature review of popular face recognition methods including both traditional (geometry-based, holistic, feature-based and hybrid methods) and deep learning methods.