Facial Features Extraction And Tracking

The contribution of this thesis is facial feature extraction and tracking.Facial feature extraction and tracking is a key step for many applications such as iris recognition, facial expression recognition, human machine interface and biometric identification.In chapter II, a face detection algorithm based on modified color space is introduced. The algorithm can detect skin region rapidly. With the modified color space, eye region can also be extracted accurately.To extract eye features, three flexible, reliable and less complex algorithms were proposed. The first is based on Snake. Snake is a successful model for contour extraction. The algorithm first builds a distance force field of eye region, then a Snake deformation approach is used to extract eye contours. A correction procedure is constructed during deformation, and two end-to-end parabolas outside the candidate are used for initialization and Snake deformation. The second is based on Hough Transform. A modified circle Hough Transform is used to extract eye features including iris contours, eyelid contours and eye corners. Due to the use of the gradient information, the algorithm can reduce the combinatorial complexity. The third algorithm for eye feature extraction is directly based on the gradient information. We have tested the three algorithms on a large image database and achieved good performances.In chapter IV, a modified Mean Shift algorithm is proposed for face tracking. This algorithm uses combinative target model. Compared with invariable target model, this algorithm can track rotate target with complex background.