Abstract
In this paper, we present an algorithm to detect and track both frontal and side faces in video clips. By means of both learning Haar-like features of human faces and boosting the learning accuracy with InfoBoost algorithm, our algorithm can detect frontal faces in video clips. Furthermore, we map these Haar-like features to a 3D model to create the classifier that can detect both frontal and side faces. Since it is costly to detect and track faces using the 3D model, we project Haar-like features from the 3D model to a 2D space in order to generate various face orientations. By using them, we can detect even side faces in real time by only learning frontal faces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aslam, J.A.: Improving Algorithms for Boosting. In: COLT 2000. Proc. of 13th Annual Conference on Computational Learning Theory, pp. 200–207 (2000)
Chernoff, H.: A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the Sum of Observation. Ann. Math. Stat. 23, 493–509 (1952)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: ICML 1996. Proc. of 13th International Conference on Machine Learning, pp. 148–156 (1996)
Georghiades, A., Belhumeur, P., Kriegman, D.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)
Gross, R., Matthews, I., Baker, S.: Constructing and fitting Active Appearance Models with occlusion. In: FPIV 2004. Proc. of the IEEE Workshop on Face Processing in Video, IEEE Computer Society Press, Los Alamitos (2004)
Gourier, N., Hall, D.J.L.C.: Estimating Face Orientation from Robust Detection of Salient Facial Features. In: Proc. of Pointing 2004, International Workshop on Visual Observation of Deictic Gestures (2004)
Pradeep, P.P., Whelan, P.F.: Tracking of facial features using deformable triangles. In: Proc. of the SPIE - Opto-Ireland 2002: Optical Metrology, Imaging, and Machine Vision, vol. 4877, pp. 138–143 (2002)
Ross, D.A., Lim, J., Yang, M.-H.: Adaptive Probabilistic Visual Tracking with Incremental Subspace Update. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 470–482. Springer, Heidelberg (2004)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 511–518. IEEE Computer Society Press, Los Alamitos (2001)
Zhu, Z., Ji, Q.: Real Time 3D Face Pose Tracking From an Uncalibrated Camera. In: FPIV 2004. Proc. of the IEEE Workshop on Face Processing in Video, IEEE Computer Society Press, Los Alamitos (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matsuyama, J., Uehara, K. (2007). Multidirectional Face Tracking with 3D Face Model and Learning Half-Face Template. In: Braz, J., Ranchordas, A., Araújo, H., Jorge, J. (eds) Advances in Computer Graphics and Computer Vision. Communications in Computer and Information Science, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75274-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-75274-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75272-1
Online ISBN: 978-3-540-75274-5
eBook Packages: Computer ScienceComputer Science (R0)