Abstract
While there has been a great deal of research in face detection and recognition, there has been very limited work on identifying the expression on a face. Many current face detection projects use a [Viola/Jones] style “cascade” of Adaboost-based classifiers to interpret (sub)images — e.g. to identify which regions contain faces. We extend this method by learning a decision tree of such classifiers (dtc): While standard cascade classification methods will apply the same sequence of classifiers to each image, our dtc is able to select the most effective classifier at every stage, based on the outcomes of the classifiers already applied. We use dtc not only to detect faces in a test image, but to identify the expression on each face.
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
Abboud, B., Davoine, F.: Facial expression recognition and synthesis based on appearance model. In: Signal Processing and Image Communication, vol. 19(8), pp. 723–740. Elsevier, Amsterdam (2004)
Cohn, J.F., Kanade, T., Wu, T.K., Lien, Y.T., Zlochower, A.: Facial Analysis: Preliminary analysis of a new image processing based method International Society for Research in Motion, Toronto (1996)
Cohn, J.F., Zlochower, A., Lien, J., Wu, Y.T., Kanade, T.: Automated face coding: A computer vision based method of facial expression analysis. In: Seventh European Conference on Facial Expression, Measurement and Meaning, Salzburg, Austria (1997)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory, Eurocolt (1995)
Grossmann, E.: Adatree: boosting a weak classifier into a decision tree. In: IEEE Workshop on Learning in Computer Vision and Patter Recognition (2004)
Isukapalli, R., Greiner, R.: Use of Off-line Dynamic Programming for Efficient Image Interpretation. In: IJCAI, Acapulco, Mexico (August. 2003)
Liu, Y., Schmidt, K., Cohn, J.F., Mitra, S.: Facial asymmetry quantification for expression invariant human identification. Computer Vision and Image Understanding 91, 138–151 (2003)
Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Transactions on Patten Analysis and Machine Intelligence, PAMI (1998)
Roth, D., Yang, M., Ahuja, N.: A snowbased face detector. In: Neural Information Processing Systems, NIPS (2000)
Schneiderman, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: International Conference on Computer Vision, ICCV (2000)
Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR (2001)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR (2001)
Wu, J., Rehg, J.M., Mullin, M.D.: Learning a rare event detection cascade by direct feature selection. In: Proceedings of Advances in Neural Information Processing Systems, NIPS (2003)
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
Isukapalli, R., Elgammal, A., Greiner, R. (2005). Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_7
Download citation
DOI: https://doi.org/10.1007/11564386_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29229-6
Online ISBN: 978-3-540-32074-6
eBook Packages: Computer ScienceComputer Science (R0)