Active Shape Models on adaptively refined mouth emphasizing color images

Abstract

In this paper, we propose a hybrid method for lip segmentation based on normalized green-color histogram splitting and Active Shape Models (ASM). A new adaptive method for histogram splitting is applied in two steps. First, after defining a region of interest for mouth segmentation, a rough adaptive threshold selects a histogram region assuring that all pixels in that region are skin pixels. Second, based on these pixels, we build a Gaussian model which represents the skin pixels distribution and is used to obtain a refined optimal threshold for lip pixel classification. This process is used to refine the normalized green channel image for the elimination of inner distortions and gradients inside the lip region, which can misguide active contours (i.e. ASM) in the last step of the hybrid segmentation process. In the results, we present that the proposed method performed better than conventional ASM on unrefined color enhanced images or pure color-histogram based mouth segmentation.