On face detection and performance evaluation for biometrics-based face systems

Mahmoud Hassaballah, Kenji Murakami, Shun Ido

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Biometrics-based personal identification is considered as an effective method for automatically recognizing a persons identity. Face detection is a fundamental step in the biometrics-based face systems and many other applications from computer vision field. Most of the face-related applications such as face recognition and face tracking assume that the face region has been perfectly detected. To adapt a certain algorithm in these applications, evaluation of its performance is needed. Unfortunately, it is difficult to evaluate the performance of a face detection algorithm due to the lack of universal criteria in the literature. In this paper we introduce a new evaluation criterion for face detection algorithms. This criterion is based on the golden ratio definition of the perfect human face. The new evaluation criterion is simple and more realistic compared to the existing one. Using the proposed criterion five Harr-cascade classifiers provided by Intel

Original languageEnglish
Title of host publicationProceedings of the IASTED International Symposia on Imaging and Signal Processing in Healthcare and Technology, ISPHT 2011
Pages57-62
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
EventIASTED International Symposia on Imaging and Signal Processing in Healthcare and Technology, ISPHT 2011 - Washington, DC, United States
Duration: 16 May 201118 May 2011

Publication series

NameProceedings of the IASTED International Symposia on Imaging and Signal Processing in Healthcare and Technology, ISPHT 2011

Conference

ConferenceIASTED International Symposia on Imaging and Signal Processing in Healthcare and Technology, ISPHT 2011
Country/TerritoryUnited States
CityWashington, DC
Period16/05/1118/05/11

Keywords

  • Biometrics
  • Face detection
  • Golden ratio
  • Performance evaluation measures

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