Abstract
The recognition of emotions from others’ faces is a universal and fundamental skill for social interaction. Many researchers argue that there is a set of basic emotions which were preserved during evolutive process because they allow the adaption of the organisms behavior to distinct daily situations. In these sense, this paper investigates emotion recognition based on sets of facial expression elements. Different feature sets are proposed to represent the characteristics of the human face and an analysis of the performance of each one is evaluated by Machine Learning techniques. It will be shown that the use of predefined areas of the face in conjunction with angles and distances is a valid proposal to construct models for emotion classification.
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
Cohn, J.F.: Foundations of human computing: Facial expression and emotion. In: ACM Int. Conf. Multimodal Interfaces, vol. 1, pp. 610–616 (2006)
Ekman, P.: Basic emotions. Wiley (1999)
Ekman, P., Friesen, W.V., Hager, J.C.: Facial Action Coding System. A Human Face (2002)
Ekman, P., Friesen, W.V.: Unmasking the face. Malor Books, Cambridge (2003)
Frank, E., Witten, I.H.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2005)
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Hawk, S.T., van Knippenberg, A.: Presentation and validation of the Radboud Faces Database. Cognition and Emotion 24(8), 1377–1388 (2010)
Lee, H., Park, J., Chung, M.: A linear affect-expression space model and control points for mascot-type facial robots. IEEE Transactions on Robotics 23(5), 863–873 (2007)
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. In: IEEE Workshop on CVPR for Human Communicative Behavior Analysis, San Francisco, USA (2010)
Mason, R., Gunst, R., Hess, J.: Statistical design and analysis of experiments. John Wiley and Sons (1989)
Mitchell, T.: Machine Learning. McGraw Hill (1997)
Picard, R.: Affective computing. MIT Press, Boston (1997)
Russell, J.: A circumplex model of affect. Journal of Personality and Social Psychology 39, 1161–1178 (1980)
Saragih, J., Lucey, S., Cohn, J.: Deformable Model Fitting by Regularized Landmark Mean-Shift. Int. Journal of Computer Vision 91(2), 200–215 (2011)
Wessels, L., Reinders, M., Hart, A., Veenman, C., Dai, H., He, T., van’t Veer, L.: A protocol for building and evaluating predictors of disease state based on microarray data. Bioinformatics 21(19), 3755–3762 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Libralon, G.L., Romero, R.A.F. (2013). Investigating Facial Features for Identification of Emotions. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-42042-9_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
eBook Packages: Computer ScienceComputer Science (R0)