Abstract
This article presents novel and robust framework for automatic recognition of facial expressions for children. The proposed framework also achieved results better than state of the art methods for stimuli containing adult faces. The proposed framework extract features only from perceptual salient facial regions as it gets its inspiration from human visual system. In this study we are proposing novel shape descriptor, facial landmark points triangles ratio (LPTR). The framework was first tested on the “Dartmouth database of children’s faces” which contains photographs of children between 6 and 16 years of age and achieved promising results. Later we tested proposed framework on Cohn-Kanade (CK+) posed facial expression database (adult faces) and obtained results that exceeds state of the art.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ekman, P.: Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage, 3rd edn. W. W. Norton & Company, New York (2001)
Pantic, M., Pentland, A., Nijholt, A., Huang, T.: Human computing and machine understanding of human behavior: A survey. In: ACM International Conference on Multimodal Interfaces (2006)
Ekman, P.: Universals and cultural differences in facial expressions of emotion. In: Nebraska Symposium on Motivation, pp. 207–283 (1971)
Littlewort, G., Bartlett, M.S., Fasel, I., Susskind, J., Movellan, J.: Dynamics of facial expression extracted automatically from video. Image Vis. Comput. 24, 615–625 (2006)
Khan, R., Meyer, A., Konik, H., Bouakaz, S.: Facial expression recognition using entropy and brightness features. In: 11th International Conference on Intelligent Systems Design and Applications (2011)
Tian, Y.: Evaluation of face resolution for expression analysis. Comput. Vis. Pattern Recogn. Workshop 68, 179–201 (2004)
Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Human vision inspired framework for facial expressions recognition. In: IEEE International Conference on Image Processing (2012)
Zhang, Y., Ji, Q.: Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 27, 699–714 (2005)
Valstar, M., Patras, I., Pantic, M.: Facial action unit detection using probabilistic actively learned support vector machines on tracked facial point data. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 76–84 (2005)
Khan, R., Meyer, A., Konik, H., Bouakaz, S.: Pain detection through shape and appearance features. In: 2013 IEEE International Conference on Multimedia and Expo (ICME) (2013)
Bai, Y., Guo, L., Jin, L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: International Conference on Image Processing (2009)
Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Framework for reliable, real-time facial expression recognition for low resolution images. Pattern Recogn. Lett. 34, 1159–1168 (2013)
Zhaoping, L.: Theoretical understanding of the early visual processes by data compression and data selection. Netw. Comput. Neural Syst. 17, 301–334 (2006)
Khan, R.A., Meyer, A., Konik, H., Bouakaz, S.: Exploring human visual system: study to aid the development of automatic facial expression recognition framework. In: Computer Vision and Pattern Recognition Workshop (2012)
Lucey, P., Cohn, J., Matthews, I., Lucey, S., Sridharan, S., Howlett, J., Prkachin, K.: Automatically detecting pain in video through facial action units. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 41, 664–674 (2011)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Fourth IEEE International Conference on Automatic face and Gesture Recognition (FG 2000), pp. 46–53 (2000)
Yang, P., Liu, Q., Metaxas, D.N.: Exploring facial expressions with compositional features. In: Computer Vision Pattern Recognition, pp. 2638–2644 (2010)
Ekman, P., Friesen, W.: The facial action coding system: A technique for the measurement of facial movements. Consulting Psychologist, Palo Alto (1978)
Pantic, M., Valstar, M.F., Rademaker, R., Maat, L.: Web-based database for facial expression analysis. In: International Conference on Multimedia and Expo (2005)
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended cohn-kande dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression. In: Computer Vision and Pattern Recognition Workshops (2010)
Dalrymple, K.A., Gomez, J., Duchaine, B.: The Dartmouth database of children’s faces: Acquisition and validation of a new face stimulus set. PLoS ONE 8, e79131 (2013)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)
Silva, C., Schnitman, L., Oliveira, L.: Detection of facial landmarks using local-based information. In: Brazilian Conference on Automation (2012)
Egger, H., Pine, D., Nelson, E., Leibenluft, E., Ernst, M., K.E., T., Angold, A.: The NIMH child emotional faces picture set (NIMH-ChEFS): A new set of children’s facial emotion stimuli. Int. J. Methods Psychiatr. Res. 20(3), 145–56 (2011)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 915–928 (2007)
Kotsia, I., Zafeiriou, S., Pitas, I.: Texture and shape information fusion for facial expression and facial action unit recognition. Pattern Recogn. 41, 833–851 (2008)
Acknowledgment
This study is financially supported by BPI France (http://www.bpifrance.fr/) and FUI-KURIO EYE Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Khan, R.A., Meyer, A., Bouakaz, S. (2015). Automatic Affect Analysis: From Children to Adults. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-27863-6_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27862-9
Online ISBN: 978-3-319-27863-6
eBook Packages: Computer ScienceComputer Science (R0)