Facial feature extraction using adaptive Hough transform, template matching and active contour models
A. Nikolaidis, Constantine L. Kotropoulos, Ioannis Pitas · 2002
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.