Going Deeper Into Face Detection: A Survey

TL;DR

This survey reviews the evolution of face detection methods from traditional classifiers to deep learning frameworks, highlighting their architectures, datasets, challenges, and future research directions.

Contribution

It provides a comprehensive overview of deep learning-based face detection methods, categorizing architectures and discussing current challenges and future prospects.

Findings

Deep learning methods significantly outperform traditional classifiers.

Major face detection architectures are categorized and analyzed.

The survey identifies key datasets and future research challenges.

Abstract

Face detection is a crucial first step in many facial recognition and face analysis systems. Early approaches for face detection were mainly based on classifiers built on top of hand-crafted features extracted from local image regions, such as Haar Cascades and Histogram of Oriented Gradients. However, these approaches were not powerful enough to achieve a high accuracy on images of from uncontrolled environments. With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection. Inspired by the rapid progress of deep learning in computer vision, many deep learning based frameworks have been proposed for face detection over the past few years, achieving significant improvements in accuracy. In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks. We also describe some of the most popular face detection datasets. Finally, we discuss some current challenges in the field, and suggest potential future research directions.