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Skin Color-Based Video Segmentation under Time-Varying Illumination

Summary: Skin Color-Based Video Segmentation
under Time-Varying Illumination
Leonid Sigal, Student Member, IEEE, Stan Sclaroff, Member, IEEE, and Vassilis Athitsos
Abstract--A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin
segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution
of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation
and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling,
and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and
resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood
Estimation and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that
obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged
experiments and popular movies. An overall increase in segmentation accuracy of up to 24 percent is observed in 17 out of 21 test
sequences. In all but one case, the skin-color classification rates for our system were higher, with background classification rates
comparable to those of the static segmentation.
Index Terms--Color video segmentation, human skin detection, dynamic Markov model.

LOCATING and tracking patches of skin-colored pixels
through an image sequence is a tool used in many face
recognition and gesture tracking systems [1], [4], [5], [7], [8],


Source: Athitsos, Vassilis - Department of Computer Science and Engineering, University of Texas at Arlington


Collections: Computer Technologies and Information Sciences