HEAD-SHOULDER HUMAN CONTOUR ESTIMATION IN STILL IMAGES

In this paper we propose a head-shoulder contour estimation model for human figures in still images, captured in a frontal pose. The contour estimation is guided by a learned head-shoulder shape model, initialized automatically by a face detector. A graph is generated around the detected face with an omega-like shape, and the estimated head-shoulder contour is a path in the graph with maximal cost. A dataset with labeled data is used to create the head-shoulder shape model and to quantitatively analyze the results. The proposed model is scaled according to the detected face size to be scale invariant. Experimental results indicate that the proposed technique works well in non trivial images, effectively estimating the contour of the head-shoulder even under partial occlusions.