COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK

Juan Wachs, Helman Stern, Mark Last

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.

Original languageEnglish
Pages (from-to)587-601
Number of pages15
JournalInternational Journal of Image and Graphics
Volume2
Issue number4
DOIs
StatePublished - 1 Oct 2002

Keywords

  • Skin segmentation
  • color recognition
  • face detection
  • fuzzy clustering
  • fuzzy logic
  • fuzzy neural networks
  • pattern recognition

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