Abstract
This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also realtime capable and automatic.
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
Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1424–1445 (2000)
Edwards, G.J., Cootes, T.F., Taylor, C.J.: Face Recognition Using Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 581–695. Springer, Heidelberg (1998)
Belheumeur, P.N., Hespanha, J.P., Kreigman, D.J.: Eigenfaces vs Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Transaction on Pattern Analysis and Machine Intelligence 19(7) (July 1997)
Wimmer, M., Stulp, F., Tschechne, S., Radig, B.: Learning Robust Objective Functions for Model Fitting in Image Understanding Applications. In: Proceedings of the 17th British Machine Vision Conference, BMVA, Edinburgh, UK, pp. 1159–1168 (2006)
Cootes, T.F., Taylor, C.J.: Active shape models – smart snakes. In: Proceedings of the 3rd British Machine Vision Conference, pp. 266–275. Springer, Heidelberg (1992)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)
Ahlberg, J.: An Experiment on 3D Face Model Adaptation using the Active Appearance Algorithm. Image Coding Group, Dept. of Electric Engineering Linköping University
Bronstein, A., Bronstein, M., Kimmel, M., Spira, A.: 3D face recognition without facial surface reconstruction. In: Proceedings of European Conference of Computer Vision, Prague, Czech Republic, May 11-14 (2004)
Park, U., Jain, A.K.: 3D Model-Based Face Recognition in Video. In: 2nd International Conference on Biometrics, Seoul, Korea (2007)
Li, S.Z., Jain, A.K.: Handbook of Face recognition. Springer, Heidelberg (2005)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2000), Grenoble, France, pp. 46–53 (2000)
Ekman, P., Friesen, W.: The Facial Action Coding System: A Technique for The Measurement of Facial Movement. Consulting Psychologists Press, San Francisco (1978)
Michel, P., Kaliouby, R.E.: Real time facial expression recognition in video using support vector machines. In: Fifth International Conference on Multimodal Interfaces, Vancouver, pp. 258–264 (2003)
Cohn, J., Zlochower, A., Lien, J.-J.J., Kanade, T.: Feature-point tracking by optical flow discriminates subtle differences in facial expression. In: Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, April 1998, pp. 396–401 (1998)
Kotsia, I., Pitaa, I.: Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transaction on Image Processing 16(1) (2007)
Riaz, Z., et al.: A Model Based Approach for Expression Invariant Face Recognition. In: 3rd International Conference on Biometrics, Italy (June 2009)
Ginneken, B., Frangi, A., Staal, J., Haar, B., Viergever, R.: Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging 21(8), 924–933 (2002)
Romdhani, S.: Face Image Analysis using a Multiple Feature Fitting Strategy. PhD thesis, University of Basel, Computer Science Department, Basel, CH (January 2005)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Riaz, Z., Mayer, C., Beetz, M., Radig, B. (2009). Model Based Analysis of Face Images for Facial Feature Extraction. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-03767-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
eBook Packages: Computer ScienceComputer Science (R0)