Facial feature extraction using adaptive Hough transform, template matching and active contour models

The present paper describes an extension to the methods proposed by Sobottka and Pitas (see Proc. of the IEEE Int. Conf. on Image Processing, Lausanne, Switzerland, p.483-6, 1996) for the extraction of facial features with the ultimate goal to be used in defining a sufficient set of distances between them so that a unique description of the structure of a face is obtained. Eyebrows, eyes, nostrils, mouth, cheeks and chin are considered as interesting features. Candidate for eyes, nostrils, mouth are determined by searching for minima and maxima on the x- and y-projections of the grey-level relief. Candidates for cheeks and chin are determined by performing adaptive Hough transform on a relevant subimage defined according to the position of the eyes and mouth and the ellipse containing the main connected components of the image. A deforming technique is also applied to the ellipse representing the main face region, in order to acquire a more accurate model of the face. Candidates for eyebrows are determined by adapting a proper grey-level template to an area restricted by the position of the eyes. The algorithms presented were tested on a set of 37 different color images containing features such as beard, glasses and changing facial expressions.