Abstract

We describe an algorithm for detecting human faces and facial features, such as the location of the eyes, nose, and mouth. First, a supervised pixel-based color classifier is employed to mark all pixels that are within a prespecified distance of "skin color," which is computed from a training set of skin patches. This color-classification map is then smoothed by Gibbs random field model-based filters to define skin regions. An ellipse model is fit to each disjoint skin region. Finally, we introduce symmetry-based cost functions to search the center of the eyes, tip of nose, and center of mouth within ellipses whose aspect ratio is similar to that of a face. Face detection facial feature detection image segmentation shape classification Gibbs random fields 1 Introduction Automatic detection and recognition of faces from still images and video is an active research area. A complete facial image analysis system should be able to localize faces in a given image, identify and pin-point fac...

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