Abstract
Human facial expressions - a visually explicit manifestation of human emotions - convey a wealth of social signals. They are often considered as the short cut to reveal the psychological consequences and mechanisms underlying the emotional modulation of cognition. However, how to analyze emotional facial expressions from the visual cortical system’s viewpoint, thus, how visual system handles facial expression information, remains elusive. As an important paradigm for understanding hierarchical processing in the ventral pathway, we report results by applying a hierarchy cortical model proposed by Poggio et al to analyze facial cues on several facial expression databases, showing that the method is accurate and satisfactory, indicating that the cortical like mechanism for facial expression recognition should be exploited in great consideration.
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
Laughlin, S.B., Sejnowski, T.J.: Communication in Neuronal Networks. Science 301, 1870–1874 (2003)
Barlow, H.: Possible principles underlying the transformation of sensory messages. In: Sensory Communication, pp. 217–234 (1961)
Fukushima, K., Miyake, S.: Neocognitron, a self-organizing neural network model for a mechanism of visual pattern recognition. Lecture Notes in Biomathematics, pp. 267–285. Springer, Heidelberg (1982)
LeCun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time-Series. In: The Handbook of Brain Theory and Neural Networks. MIT Press (1995)
LeCun, Y., Bengio, Y.: Pattern Recognition and Neural Networks. In: The Handbook of Brain Theory and Neural Networks. MIT Press (1995)
Ullman, S., Soloviev, S.: Computation of pattern invariance in brain-like structures. Neural Netw. 12, 1021–1036 (1999)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermdediate complexity and their use in classification. Nat. Neurosci. 5(7), 682–687 (2002)
Wersing, H., Koerner, E.: Learning optimized features for hierarchical models of invariant recognition. Neural Comp. 15(7), 1559–1588 (2003)
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019–1025 (1999)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29, 411–426 (2007)
Li, Z.P.: Theoretical Understanding of the early visual processes by data compression and data selection. Network: Computation in Neural System 17(4) (2006)
Serre, T.: Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines, PhD Thesis, Massachusetts Institute of Technology (2006)
JAFEE dataset, http://www.kasrl.org/jaffe.html
Cohn-Kanada AU-Coded dataset, http://vasc.ri.cmu.edu/idb/html/face
CAS-PEAL dataset, http://www.jdl.ac.cn/peal/index.html
Fu, S.Y., Hou, Z.G., Yang, G.S.: Spiking Neural Networks based Cortex Like Mechanism: A Case Study for Facial Expression Recognition. In: IJCNN (2011)
Wersing, H., Koerner, E.: Learning optimized features for hierarchical models of invariant recognition. Neural Comp. 15(7), 1559–1588 (2003)
Serre, T., Poggio, T.: Models of visual cortex. In: Scholarpedia (2012) (in revision)
Bartlett, M.S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior. In: CVPR (2005)
Erman, X., Senlin, L., Limin, P.: Turbo-Boost Facial Expression Recognition Using Haar-Like Features. Journal of Computer Aided Design and Computer Graphics 23(8) (2011) (in Chinese)
Shih, F.Y., Chuang, C.F., Wang, P.P.: Performance Comparison of Facial Expression Recongition in JAFFE Database. International Journal of Pattern Recognition and Artificial Intelligence 22(3), 445–459 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, T., Yang, G., Kuai, X. (2012). On Cortex Mechanism Hierarchy Model for Facial Expression Recognition: Multi-database Evaluation Results. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-31362-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
eBook Packages: Computer ScienceComputer Science (R0)