Survey of face detection on low-quality images

Yuqian Zhou, Ding Liu, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Face detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems. In this paper, we first review the current state-of-the-art face detectors and their performance on benchmark dataset FDDB, and compare the design protocols of the algorithms. Secondly, we investigate their performance degradation while testing on low-quality images with different levels of blur, noise, and contrast. Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. It inspires researchers to produce more robust design for face detection in the wild.

Original languageEnglish (US)
Title of host publicationProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages769-773
Number of pages5
ISBN (Electronic)9781538623350
DOIs
StatePublished - Jun 5 2018
Event13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 - Xi'an, China
Duration: May 15 2018May 19 2018

Publication series

NameProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018

Other

Other13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Country/TerritoryChina
CityXi'an
Period5/15/185/19/18

Keywords

  • Face Detection
  • Low-quality

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Survey of face detection on low-quality images'. Together they form a unique fingerprint.

Cite this