Automatic and efficient 3D face feature segmentation with active contours

3D face analysis is a field of growing interest in the applied Computer Vision community, its applications including face recognition, modelling and biometrics, virtual and augmented reality. While several works exist on the extraction of feature points from 3D face data, there is so far no automatic system for 3D face feature contour segmentation. This paper focuses on the later problem: we aim to fill this gap. Starting from a 3D range image, bounding boxes for the face features of interest are determined first. Then the feature boundaries are segmented accurately using globally optimal and quasi-optimal active contour (AC) methods. Both AC approaches incorporate shape priors to make features more robust under noise. Evaluation on a test database shows that the proposed system yields accurate segmentation results of lip and eye contours for 96% and 85% of the datasets, respectively.