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.
Lee et al. REF propose a generative model that infers a set of hypotheses for the face, hair, and background regions.
Markov random field models for hair and face segmentation
2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
Mathematics Computer Science