Abstract
An approach for segmenting and tracking a face in a sequence of color images is presented. It enables reliable segmentation of facial region despite variation of skin-color perceived by a camera. A second order Markov model is utilized to forecast the skin distribution of facial regions in the next frame. The histograms that are constructed from the predicted distribution are backprojected to generate candidates of facial regions. Afterwards, a connected component labeling takes place. Spatial morphological operations, such as size and hole filtering are employed next. The Active Shape Model seeks to match a set of model points to the image. This statistical model of shape supports the segmentation of facial region undergoing tracking. Histograms are accommodated over time using feedback from shape, newly classified skin pixels and predictions of the skin-color evolution. This evolution is described by translation, rotation and scaling. In this context, the novelty of our approach lies in the introduction of Active Shape Model dealing with translation, rotation and scaling of the target to support face verification as well as to guide the evolution of skin distribution. The kernel histograms characterize the face during tracking in subsequent frames. The proposed algorithm achieves reliable detection and tracking results. The resulting system runs in real-time on standard PC computer.
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References
Birchfield, S.: Elliptical Head Tracking Using Intensity Gradients and Color Histograms. In: Proc. IEEE Conf. on Comp. Vis. and Patt. Rec., pp. 232–237 (1998)
Blake, A., Isard, M., Reynard, D.: Learning to Track the Visual Motion of Contours. Artificial Intelligence 78, 101–133 (1995)
Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)
Bradski, G.R.: Computer Vision Face Tracking as a Component of a Perceptual User Interface. In: Proc. IEEE Workshop on Appl. of Comp. Vision, pp. 214–219 (1998)
Chen, Y., Rui, Y., Huang, T.: Mode-based Multi-Hypothesis Head Tracking Using Parametric Contours. In: Proc. IEEE Int. Conf. on Aut. Face and Gesture Rec., pp. 112–117 (2002)
Cho, K.M., Jang, J.H., Hong, K.S.: Adaptive Skin Color Filter. Pattern Recognition 34(5), 1067–1073 (2001)
Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. In: Proc. IEEE Conf. on Comp. Vis. Patt. Rec., pp. 142–149 (2000)
Cootes, T.: An Introduction to Active Shape Models, Model-Based Methods in Analysis of Biomedical Images. In: Baldock, R., Graham, J. (eds.) Image Processing and Analysis. Oxford University Press, Oxford (2000)
Elgammal, A., Duraiswami, R., Davis, L.S.: Probabilistic Tracing in Joint Feature-Spatial Spaces. In: Proc. IEEE Conf. on Comp. Vis. and Patt. Rec., pp. 16–22 (2003)
Fieguth, P., Terzopoulos, D.: Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates. In: Proc. IEEE Conf. on Comp. Vis. and Patt. Rec., Hilton Head Island, pp. 21–27 (1997)
Han, B., Davis, L.: Robust Observations for Object Tracking. In: Proc. Int. Conf. on Image Processing, pp. 442–445 (2005)
Isard, M., Blake, A.: Contour Tracking by Stochastic Propagation of Conditional Density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 343–356. Springer, Heidelberg (1996)
Koschan, A., Kang, A., Paik, J., Abidi, B., Abidi, M.: Color Active Shape Models for Tracking Non-Rigid Objects. Pattern Recognition Letters 24, 1751–1765 (2003)
Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Raja, Y., McKenna, S.J., Gong, S.: Color Model Selection and Adaptation in Dynamic Scenes. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 460–474. Springer, Heidelberg (1998)
Soriano, M., Martinkauppi, B., Huovinen, S., Laaksonen, M.: Adaptive Skin Color Modelling Using the Skin Locus for Selecting Training Pixels. Pattern Recognition 36, 681–690 (2003)
Soriano, M., Martinkauppi, B., Pietikainen, M.: Detection of Skin under Changing Illumination: A Comparative Study. In: Int. Conf. on Image Analysis and Proc., pp. 652–657 (2003)
Sigal, L., Sclaroff, S., Athitsos, V.: Estimation and Prediction of Evolving Color Distributions for Skin Segmentation under Varying Illumination. In: Proc. IEEE Conf. on Comp. Vis. and Patt. Rec., pp. 2152–2159 (2000)
Sobottka, K., Pitas, I.: Segmentation and Tracking of Faces in Color Images. In: Proc. of the Sec. Int. Conf. on Aut. Face and Gesture Rec., pp. 236–241 (1996)
Srisuk, S., Kurutach, W., Limpitikeat, K.: A Novel Approach for Robust Fast and Accurate Face Detection. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 9(6), 769–779 (2001)
Swain, M.J., Ballard, D.H.: Color Indexing. Int. J. of Comp. Vision 7(1), 11–32 (1991)
Yang, M.-H., Krigman, D., Ahuja, N.: Detecting Faces in Images: A survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)
Yang, J., Weier, L., Waibel, A.: Skin-Color Modelling in Color Images. In: Proc. Asian Conf. on Computer Vision, vol. II, pp. 687–694 (1998)
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Kwolek, B. (2006). Active Shape Model Based Segmentation and Tracking of Facial Regions in Color Images. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_28
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DOI: https://doi.org/10.1007/11867586_28
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