Color Image Of The Face Detection And Tracking

In this paper we present a novel approach for segmentation and tracking of faces in color images. The whole algorithm is composed of three main modules.Segmentation of faces is performed by evaluating color and shape information. First, skin-like regions are determined based on Fuzzy HSCC by integrating the HS and CrCb spaces based on the concept of fuzzy membership function. Then regions with elliptical shape are selected as face hypotheses.The skin segmentation is robust against different lighting conditions and various types of skin color. They are verified by searching for facial features in their interior.Our approach for facial feature extraction is based on the observation that facial features differ from the rest of the face because of their low brightness. Thus, we detect facial features candidates by evaluating the lopographic greylevel relief of the face region. After obtaining an eye window, the eye can be located by using variance projection functions. Based on vertical symmetry distances between facial features and the assessment of each facial feature, we choose the best face constellation.After a face is reliably detected, it is tracked over time. Tracking is realized by using an active contour model. The exterior forces of the snake are defined based on color features. They push or pull snaxels perpendicular to the snake. Results demonstrate the efficiency and feasibility of this algorithm, which can be used in time critical system.