Quality labeled faces in the wild (QLFW): A database for studying face recognition in real-world environments

Lina Karam, Tong Zhu

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

9 Scopus citations

Abstract

The varying quality of face images is an important challenge that limits the effectiveness of face recognition technology when applied in real-world applications. Existing face image databases do not consider the effect of distortions that commonly occur in real-world environments. This database (QLFW) represents an initial attempt to provide a set of labeled face images spanning the wide range of quality, from no perceived impairment to strong perceived impairment for face detection and face recognition applications. Types of impairment include JPEG2000 compression, JPEG compression, additive white noise, Gaussian blur and contrast change. Subjective experiments are conducted to assess the perceived visual quality of faces under different levels and types of distortions and also to assess the human recognition performance under the considered distortions. One goal of this work is to enable automated performance evaluation of face recognition technologies in the presence of different types and levels of visual distortions. This will consequently enable the development of face recognition systems that can operate reliably on real-world visual content in the presence of real-world visual distortions. Another goal is to enable the development and assessment of visual quality metrics for face images and for face detection and recognition applications.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9394
ISBN (Print)9781628414844
DOIs
StatePublished - 2015
EventHuman Vision and Electronic Imaging XX - San Francisco, United States
Duration: Feb 9 2015Feb 12 2015

Other

OtherHuman Vision and Electronic Imaging XX
CountryUnited States
CitySan Francisco
Period2/9/152/12/15

Keywords

  • Database
  • Face recognition
  • Visual quality

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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  • Cite this

    Karam, L., & Zhu, T. (2015). Quality labeled faces in the wild (QLFW): A database for studying face recognition in real-world environments. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9394). [93940B] SPIE. https://doi.org/10.1117/12.2080393