Biometric and Color Features Fusion for Face Detection and Tracking in Natural Video Sequences
A system that performs the detection and tracking of a face in real-time in real video sequences is presented in this paper. The face is detected in a complex environment by a model of human colour skin. Very good results are obtained, since the colour segmentation removes almost all the complex background and it is realized to a very high-speed, making the system very robust. On the other hand, fast and stable real-time tracking is then achieved via biometric feature extraction of face using connected components labelling. Tracking does not require a precise initial fit of the model. Therefore, the system is initialised automatically using a very simple 2D face detector based on target ellipsoidal shape. Results are presented showing a significant improvement in detection rates when the whole sequence is used instead of a single image of the face. Experiments in tracking are reported.
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- 4.Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 232–237 (1998)Google Scholar
- 6.Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: International Conference on Computer Vision, vol. 2, pp. 734–741 (2003)Google Scholar
- 7.Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of the International Conference on Computer Vision, pp. 555–562 (1998)Google Scholar
- 8.Jones, M.J., Rehg, J.M.: Statistical Color Models with Application to Skin Detection. Tech. Rep. Cambridge Research Laboratory (1998)Google Scholar
- 9.Haralick, R.M., Shapiro, L.: Computer and Robot Vision. 1, pp. 28–48. Addison Wesley, Reading (1992)Google Scholar
- 11.Chai, D., Bouzerdoum, A.: A Bayesian approach to skin color classification in YCbCr color space. In: IEEE TENCON00, vol. 2, pp. 421–424 (2000)Google Scholar