179
Views
4
CrossRef citations to date
0
Altmetric
Articles

Modelling Facial Features for Emotion Recognition and Synthesis

&

References

  • I. Kotsia, I. Buciu and I. Pitas. (2008). An analysis of facial expression recognition under partial facial image occlusion. Image Vis. Comput. 26(7), pp. 1052–1067.
  • H. Navin and M. K. Mirnia, “A new algorithm to classify face emotions through eye and lip features by using particle swarm optimization,” in 4th International Conference on Computer Modeling and Simulation, vol. 22, 2012.
  • G. Hipp, N. J. Diederich, V. Pieria and M. Vaillant. (2014). Primary vision and facial emotion recognition in early Parkinson's disease. J. Neurol. Sci. 338(1),pp. 178–182.
  • A. Mehrabian, Silent Messages: Implicit Communication of Emotions and Attitudes. Belmont, CA: Wadsworth, 1981.
  • C. Darwin, The Expression of the Emotions in Man and Animals. New York, NY: Oxford University Press, 1998.
  • P. Ekman and W. Friesen, Facial Action Coding System: A Technique for the Measurement of Facial Movement. Palo Alto, CA: Consulting Psychologists Press, 1978.
  • P. Ekman and W. V. Friesen. (1976). Measuring facial movement. Environ. Psychol. Nonverbal Behav. 1(1), pp. 56–75.
  • Y. Li, S. Wang, Y. Zhao and Q. Ji. (2013). Simultaneous facial feature tracking and facial expression recognition. IEEE Trans. Image Process. 22(7), pp. 2559–2573.
  • M. Ilbeygi and H. Shah-Hosseini. (2012). A novel fuzzy facial expression recognition system based on facial feature extraction from color face images. Eng. Appl. Artif. Intel. 25(1), pp. 130–146.
  • E. Owusu, Y. Zhan and Q. R. Mao. (2014). A neural-adaboost based facial expression recognition system. Exp. Syst. Appl..41(7), pp. 3383–3390.
  • A. Asthana, S. Zafeiriou, G. Tzimiropoulos, S. Cheng and M. Pantic. (2014). From pixels to response maps: discriminative image filtering for face alignment in the wild. IEEE Trans. Pattern Anal. Machine Intell. PrePrint(1), pp. 1–1.
  • P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for Action Unit and emotion-specified expression," in Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
  • T. F. Cootes, C. J. Taylor, D. H. Cooper and J. Graham. (1995). Active shape models & mdash; their training and application. Comput. Vis. Image Und. 61(1), pp. 38–59.
  • T. F. Cootes, G. J. Edwards and C. J. Taylor, Active Appearance Models. Berlin, Heidelberg: Springer, 1998.
  • J. M. Saragih, S. Lucey and J. F. Cohn. (2011). Deformable model fitting by regularized landmark mean-shift. Int. J. Comput. Vis. 91(2), pp. 200–215.
  • F. L. Bookstein. (1989). Principal warps: thin-plate splines and the decomposition of deformations. IEEE T Pattern Anal. 11(6), pp. 567–585.
  • J. McCall and M. Trivedi. (2004). “Pose invariant affect analysis using thin-plate splines,” IEEE Int. Conf. Pattern Recogn. (ICPR), 3, pp. 958–964.
  • N. Rathee and D. Ganotra. (2015). “A novel approach for pain intensity detection based on facial feature deformations,” J. Vis. Commun. Image Representation, 33, pp. 247–254.
  • O. C. Ramirez Rivera, Rojas Castillo. (2013). Local directional number pattern for face analysis: face and expression recognition. IEEE Trans. Image Proces. 22(5), pp. 1740–1752.
  • M. Kotti and F. Paterno. (2012). Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema. Int. J. Speech. Technol. 15, pp. 131–150.
  • M. Sheikhan, M. Bejani and D. Gharavian. (2013). Modular neural-svm scheme for speech emotion recognition using anova feature selection method. Pattern Recogn. 47(3), pp. 215–227.
  • C. J. C. Burges. 1998. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc. 2(2), pp. 121–167.
  • M. I. Faraj and J. Bigun. (2007). Synergy of lip-motion and acoustic features in biometric speech and speaker recognition. IEEE Trans. Comput. 56(9), pp. 1169–1175.
  • C. C. Chang and C. -J. Lin. (2011). LIBSVM: a library for support vector machines. ACM Trans. Intel Syst Technol. 2(3), pp. 27:1–27:27.
  • M. T. Eskil and K. S. Benli. (2014). Facial expression recognition based on anatomy. Comput Vis Image Und. 119(4), pp. 1–14.
  • N. Sebe, M. S. Lew, Y. Sun, I. Cohen, T. Gevers, and T. S. Huang. (2007). Authentic facial expression analysis.” Image Vis Comput. 25(12), pp. 1856–1863.
  • A. Majumder, L. Behera and V. K. Subramanian. (2014). Emotion recognition from geometric facial features using self-organizing map. Pattern Recogn. 47(3), pp. 1282–1293.
  • N. Rathee and D. Ganotra. (in Press). Discriminative analysis of lip features for emotion recognition. Int. J. Comput. Vis. Robotics.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.