Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions
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 gibbs random fields facial feature detection image segmentation shape classification