An accurate algorithm for head detection based on XYZ and HSV hair and skin color models

Abstract

Head detection in images and videos plays an important role in a wide range of computer vision and multimedia applications. In this paper, we propose a new head detection algorithm that is capable of handling significantly variable conditions in terms of viewpoint (i.e. frontal, profile, back view, from -180 degrees to +180 degrees), tilt angle (i.e. from horizontal to aerial), scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. The appearance distribution models the colors of hair and skin by sets of Gaussian mixtures in the XYZ and HSV color spaces. The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on the human anatomy. This presents a pixel-level measurement of accuracy for the proposed algorithm both prior and after applying the spatial constraints referenced by the elliptical model. The excellent accuracy at both levels confirms the accuracy of the appearance model and the appropriateness of the spatial and topological process.