Abstract
We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.
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.
Similar content being viewed by others
References
Schwerdt, K., Crowley, J.L.: Robust Face Tracking Using Colour. In: IEEE Int’l Conf. Automatic Face and Gesture Recognition, pp. 90–95 (2000)
Birchfield, S.: Elliptical Head Tracking using Intensity Gradient and Color Histograms. IEEE Computer Vision and Pattern Recognition, 232–237 (1998)
Hager, G., Toyama, K.: X Vision: A Portable Substrate for Real-Time Vision Applications. Computer Vision and Image Understanding 69(1), 23–37 (1998)
Yao, Z., Li, H.: Tracking a Detected Face with Dynamic Programming. Image and Vision Computing 24(6), 573–580 (2006)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, pp. 356–395. Prentice-Hall, Englewood Cliffs (2002)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (2001)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)
MIT CBCL - Face Database, http://www.ai.mit.edu/projects/cbcl/
Welch, G., Bishop, G.: An Introduction to the Kalman filter. University of North Carolina at Chapel Hill, Department of Computer Science, TR 95-041 (2004)
Open-Video, http://www.open-video.com
Boston University IVC Head Tracking Video Set, http://www.cs.bu.edu/groups/ivc/
Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: IEEE Int’l Conf. Image Processing, vol. 1, pp. 900–903 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Seong, CY., Kang, BD., Kim, JH., Kim, SK. (2006). Effective Detector and Kalman Filter Based Robust Face Tracking System. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_45
Download citation
DOI: https://doi.org/10.1007/11949534_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
eBook Packages: Computer ScienceComputer Science (R0)