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FGR
2008
IEEE

Markov random field models for hair and face segmentation

9 years 4 months ago
Markov random field models for hair and face segmentation
This paper presents an algorithm for measuring hair and face appearance in 2D images. Our approach starts by using learned mixture models of color and location information to suggest the hypotheses of the face, hair, and background regions. In turn, the image gradient information is used to generate the likely suggestions in the neighboring image regions. Either Graph-Cut or Loopy Belief Propagation algorithm is then applied to optimize the resulting Markov network in order to obtain the most likely hair and face segmentation from the background. We demonstrate that our algorithm can precisely identify the hair and face regions from a large dataset of face images automatically detected by the state-of-the-art face detector.
Kuang-chih Lee, Dragomir Anguelov, Baris Sumengen,
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where FGR
Authors Kuang-chih Lee, Dragomir Anguelov, Baris Sumengen, Salih Burak Göktürk
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