Robust face detection using discriminating feature analysis and bayes classifier

Research output: Contribution to journalConference articlepeer-review


This paper presents a novel face detection method, which integrates the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1-D Haar wavelet representation, and its amplitude projections. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. Finally, the Bayes classifier applies the estimated conditional PDFs to detect multiple frontal faces in an image. Experimental results using 853 images (containing a total of 970 faces) from diverse image sources show the feasibility of the proposed face detection method.

Original languageEnglish (US)
Article number19
Pages (from-to)164-172
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2005
EventBiometric Technology for Human Identification II - Orlando, FL, United States
Duration: Mar 28 2005Mar 29 2005

All Science Journal Classification (ASJC) codes

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


  • Bayes classifier
  • Discriminating feature analysis
  • Face detection
  • Haar wavelet
  • Statistical modeling


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