research-article

A survey on face detection in the wild

Authors:

Stefanos Zafeiriou,

Zhengyou ZhangAuthors Info & Claims

Computer Vision and Image Understanding, Volume 138, Issue C

Pages 1 - 24

https://doi.org/10.1016/j.cviu.2015.03.015

Published: 01 September 2015 Publication History

Abstract

We present a comprehensive and survey for face detection 'in-the-wild'.We critically describe the advances in the three main families of algorithms.We comment on the performance of the state-of-the-art in the current benchmarks.We outline future research avenues on the topic and beyond. Face detection is one of the most studied topics in computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. During the past 15years, tremendous progress has been made due to the availability of data in unconstrained capture conditions (so-called 'in-the-wild') through the Internet, the effort made by the community to develop publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we survey the recent advances in real-world face detection techniques, beginning with the seminal Viola-Jones face detector methodology. These techniques are roughly categorized into two general schemes: rigid templates, learned mainly via boosting based methods or by the application of deep neural networks, and deformable models that describe the face by its parts. Representative methods will be described in detail, along with a few additional successful methods that we briefly go through at the end. Finally, we survey the main databases used for the evaluation of face detection algorithms and recent benchmarking efforts, and discuss the future of face detection.

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cover image Computer Vision and Image Understanding

Computer Vision and Image Understanding Volume 138, Issue C

September 2015

125 pages

ISSN:1077-3142

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Published: 01 September 2015

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Cloude EDindar MNinaus MKiili K(2024)Synchrony Between Facial Expressions and Heart Rate Variability During Game-Based Learning: Insights from Cross-Wavelet TransformationTechnology Enhanced Learning for Inclusive and Equitable Quality Education10.1007/978-3-031-72315-5_7(90-104)Online publication date: 16-Sep-2024
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