A Novel Real-Time Facial Expression Capturing Method

Abstract:

In this paper, we propose a new facial expression capturing method in real-time mode. Our proposed method main includes two processes: 1) Online process and 2) Offline process. The offline process adopt human face database to build 2D face model and 3D shape model, and then train an expression feature model. Afterwards, online process extracts feature points from human face images, and then obtains facial expression by SVM classifier which is trained from offline process. The main creativity of our method lies in that we propose an effective face detection approach and propose an optimal evaluation method to facial expression recognition. Experimental results show that our approach can capture facial expression precisely in real-time mode.

• Periodical: Applied Mechanics and Materials (Volume 273)
• Pages: 826-830
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.273.826
• Online since: January 2013
• Authors: Yong Sheng Wang
• Keywords: Expression Feature Model, Facial Expression, Offline Process, Online Process

References

[1] Ekman, P. and Rolls, ET and Perrett, DI and Ellis, HD, Facial Expressions of Emotion: an Old Controversy and New Findings, Philosophical Transactions of the Royal Society, 1992, 335, pp.63-69.

DOI: 10.1098/rstb.1992.0008

[2] P. Ekman and W. V. Friesen, Constant across cultures in the face and emotion, Journal of personality and social psychology, 1972, 17(2), pp.124-129.

DOI: 10.1037/h0030377

[3] P. Ekman, W. V. Friesen, and J. C. Hager, Facial Action Coding System. Salt Lake City, UT: Human Face, (2002).

[4] J. J. Lien, T. Kanade, J. F. Cohn, and C. -C. Li, Subtly different facial expression recognition and expression intensity estimation, in Proc. IEEE International Conference Computer Vision and Pattern Recognition, 1998, pp.853-859.

DOI: 10.1109/cvpr.1998.698704

[5] Y. Gao, S. H. Leung, and H. Leung, Facial expression recognition from line-based caricatures, IEEE Transactions on Man and Cybernetics, Part A: Systems and Humans, 2003, 33(3), pp.407-412.

DOI: 10.1109/tsmca.2003.817057

[6] Y. Zhang and Q. Ji, Active and dynamic information fusion for facial expression understanding from image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5), pp.699-714.

DOI: 10.1109/tpami.2005.93

[7] Y. Tian, T. Kanade, and J. F. Cohn, Recognizing action units for facial expression analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2), pp.97-115.

DOI: 10.1109/34.908962

[8] A. M. Martínez, Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(6), pp.748-763.

DOI: 10.1109/tpami.2002.1008382

[9] Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi & Jiro Gyoba. Coding Facial Expressions with Gabor Wavelets. Proceedings, Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16 1998, Nara Japan, IEEE Computer Society, pp.200-205.

DOI: 10.1109/afgr.1998.670949

[10] Michael J. Lyons, Julien Budynek, & Shigeru Akamatsu. Automatic Classification of Single Facial Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (12), pp.1357-1362 (1999).

DOI: 10.1109/34.817413

[11] Matthew N Dailey, Carrie Joyce, Michael J Lyons, Miyuki Kamachi, Hanae Ishi, Jiro Gyoba, & Garrison W Cottrell. Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion, Vol 10(6), 2010, pp.874-893.

DOI: 10.1037/a0020019