Abstract
In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, the weights of weak classifiers may not be optimized. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which yields better generalization performance.
Chapter PDF
Similar content being viewed by others
References
Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 34–58 (2002)
Rowly, H., Baluja, S., Kanade, T.: Neural network-based face detection. PAMI 20, 23–38 (1998)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. IEEE CVPR, 511–518 (2001)
Romdhani, S., Torr, P., Schoelkopf, B., Blake, A.: Computationally efficient face detection. In: Proc. Intl. Conf. Computer Vision, pp. 695–700 (2001)
Henry, S., Takeo, K.: A statistical model for 3d object detection applied to faces and cars. In: IEEE Conference on Computer Vision and Pattern Recognition (2000)
Freund, Y., Schapire, R.: A diction-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)
Li, S.Z., Zhang, Z.Q., Harry, S., Zhang, H.J.: FloatBoost learning for classification. In: Proc.CVPR, pp. 511–518 (2001)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Technical report, MRL, Intel Labs (2002)
Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: NIPS, vol. 14 (2002)
Sung, K.K.: Learning and Example Selection for Object and Pattern Detection. PhD thesis, MIT AI Lab (January 1996)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)
Carbonetto, P.: Viola training data (Database), http://www.cs.ubc.ca/~pcarbo
http://cbcl.mit.edu/projects/cbcl/software-datasets/FaceData1Readme.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ou, Z., Tang, X., Su, T., Zhao, P. (2005). Cascade AdaBoost Classifiers with Stage Optimization for Face Detection. In: Zhang, D., Jain, A.K. (eds) Advances in Biometrics. ICB 2006. Lecture Notes in Computer Science, vol 3832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11608288_17
Download citation
DOI: https://doi.org/10.1007/11608288_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31111-9
Online ISBN: 978-3-540-31621-3
eBook Packages: Computer ScienceComputer Science (R0)
