Abstract
This work proposes new static and dynamic based methods for facial expression recognition in stereo image sequences. Computer vision 3-d techniques are applied to determine real world geometric measures and to build a static geometric feature vector. Optical flow based motion detection is also carried out which delivers the dynamic flow feature vector. Support vector machine classification is used to recognize the expression using geometric feature vector while k-nearest neighbor classification is used for flow feature vector. The proposed method achieves robust feature detection and expression classification besides covering the in/out of plane head rotations and back and forth movements. Further, a wide range of human skin color is exploited in the training and the test samples.
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References
Li, S.Z., Jain, A.K.: Handbook of Face Recognition (2005) ISBN: 0-387-40595-X
Black, M.J., Yacoob, Y.: Recognizing facial expressions in image sequences using local parameterized models of image motion. Int. Journal of CV 25(1), 23–48 (1997)
Valstar, M.F., Pantic, M.: Fully automatic facial action unit detection and temporal analysis. In: Proceedings of IEEE Int. Conf. Computer Vision and Pattern Recognition (2006)
Kumano, S., Otsuka, K., Yamato, J., Eisaku, S., Sata, Y.: Pose-Invariant facial expression recognition using variable intensity templates. In: Asian Conf. on Computer Vision (2007)
Bartlett, M.S., Littlewort, G., Frank, M.G., Lainscsek, C., Fasel, I., Movellan, J.: Fully automatic facial action recognition in spontaneous behavior. In: Proc. Conf. Automatic Face&Gesture Recognition, pp. 223–230 (2006)
Hu, C., Chang, Y., Feris, R., Turk, M.: Manifold based analysis of facial expression. Image and Vision Computing 24, 605–614 (2006)
Zeng, Z., Fu, Y., Roisman, G.I., Zhen, W.: Spontaneous emotional facial expression detection. Journal of Multimedia (2006)
Torre, F., Campoy, J., Ambadar, Z., Cohn, J.F.: Temporal Segmentation of Facial Behavior. In: International Conference on Computer Vision (October 2007)
Niese, R., Al-Hamadi, A., Michaelis, B.: Nearest Neighbor Classification for Emotion Recognition in Stereo Image Sequences. ISAST Transactions on Electronics and Signal Processing, No 1(1), 88–94 (2007)
Niese, R., Al-Hamadi, A., Panning, A., Michaelis, B.: Real-time Capable Method for Facial Expression Recognition in Color and Stereo Vision. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part I. LNCS, vol. 4705, pp. 397–408. Springer, Heidelberg (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (2001)
Gonzalez, C.R., Woods, E.R.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)
Lucas, B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: 7th Inter. Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and other kernel based learning methods (2001) ISBN: 0-521-78019-X
Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (2006) ISBN:978-0-262-19547-8
Lin, C.-J., Weng, C.R.: Simple Probabilistic Predictions for Support Vector Regression. Technical report, Department of Computer Science, National Taiwan University (2004)
Byrd, R., Balaji, B.: Real time 2D face detection using color rations and k-mean clustering. In: Proc. of the 44th Southeast regional conference, Florida, pp. 644–648 (2006)
Fraunhofer-Institut fuer Integrierte Schaltungen IIS, Schaltungen IIS, Erlangen, Germany, Biometrics Demo, http://www.iis.fraunhofer.de/EN/bf/bv/kognitiv/biom/dd.jsp
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Al-Hamadi, A., Niese, R., Pathan, S.S., Michaelis, B. (2009). Geometric and Optical Flow Based Method for Facial Expression Recognition in Color Image Sequences. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2008. Lecture Notes in Computer Science, vol 5337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02345-3_23
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DOI: https://doi.org/10.1007/978-3-642-02345-3_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02344-6
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