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Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 946589, 13 pages
http://dx.doi.org/10.1155/2012/946589
Research Article

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

School of Information and Engineering, The Central University of Nationalities, Beijing 100081, China

Received 27 April 2012; Accepted 3 July 2012

Academic Editor: Long Cheng

Copyright © 2012 Si-Yao Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. B. Laughlin and T. J. Sejnowski, “Communication in neuronal networks,” Science, vol. 301, no. 5641, pp. 1870–1874, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Barlow, “Possible principles underlying the transformation of sensory messages,” Sensory Communication, pp. 217–2234, 1961.
  3. K. Fukushima and S. Miyake, Neocognitron, a Self-Organizing Neural Network Model for A Mechanism of Visual Pattern Recognition, Lecture Notes in Biomathematics, Springer, 1982.
  4. Y. LeCun and Y. Bengio:, Convolutional Networks for Images, Speech, and Time-Series. The Handbook of Brain theory and Neural Networks, MIT Press, 1995.
  5. Y. LeCun and Y. Bengio, Pattern Recognition and Neural Networks. The Handbook of Brain theory and Neural Networks, MIT Press, 1995.
  6. S. Ullman and S. Soloviev, “Computation of pattern invariance in brain-like structures,” Neural Networks, vol. 12, no. 7-8, pp. 1021–1036, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Ullman, M. Vidal-Naquet, and E. Sali, “Visual features of intermediate complexity and their use in classification,” Nature Neuroscience, vol. 5, no. 7, pp. 682–687, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Wersing and E. Körner, “Learning optimized features for hierarchical models of invariant object recognition,” Neural Computation, vol. 15, no. 7, pp. 1559–1588, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Riesenhuber and T. Poggio, “Hierarchical models of object recognition in cortex,” Nature Neuroscience, vol. 2, no. 11, pp. 1019–1025, 1999. View at Publisher · View at Google Scholar · View at Scopus
  10. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust object recognition with cortex-like mechanisms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 411–426, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, and T. Poggio, Theory of Object Recognition: computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex, AI Memo 2005-036/CBCL Memo 259, MIT Press, Cambridge, Mass, USA.
  12. A. L. HODGKIN and A. F. HUXLEY, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” The Journal of Physiology, vol. 117, no. 4, pp. 500–544, 1952. View at Scopus
  13. W. Gerstern and W. M. Kistler, Spiking Neuron Models, Cambridge University Press, 2002.
  14. E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1569–1572, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Thorpe, D. Fize, and C. Marlot, “Speed of processing in the human visual system,” Nature, vol. 381, no. 6582, pp. 520–522, 1996. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Delorme, J. Gautrais, R. Van Rullen, and S. Thorpe, “SpikeNET: a simulator for modeling large networks of integrate and fire neurons,” Neurocomputing, vol. 26-27, pp. 989–996, 1999. View at Publisher · View at Google Scholar · View at Scopus
  17. S. G. Wysoski, L. Benuskova, and N. Kasabov, “Fast and adaptive network of spiking neurons for multi-view visual pattern recognition,” Neurocomputing, vol. 71, no. 13-15, pp. 2563–2575, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. S. G. Wysoski, L. Benuskova, and N. Kasabov, “Evolving spiking neural networks for audiovisual information processing,” Neural Networks, vol. 23, no. 7, pp. 819–835, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. R. J. Dolan, “Neuroscience and psychology: emotion, cognition, and behavior,” Science, vol. 298, no. 5596, pp. 1191–1194, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. M. N. Dailey, G. W. Cottrell, C. Padgett, and R. Adolphs, “Empath: a neural network that categorizes facial expressions,” Journal of Cognitive Neuroscience, vol. 14, no. 8, pp. 1158–1173, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. M. N. Dailey, C. Joyce, M. J. Lyons et al., “Evidence and a computational explanation of cultural differences in facial expression recognition,” Emotion, vol. 10, no. 6, pp. 874–893, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. S.-Y. Fu, G.-S. Yang, and Z.-G. Hou, “Spiking neural networks based cortex like mechanism: a case study for facial expression recognition,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '11), pp. 1637–1642, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Zhaoping, “Theoretical understanding of the early visual processes by data compression and data selection,” Network: Computation in Neural Systems, vol. 17, no. 4, pp. 301–334, 2006. View at Scopus
  24. T. Serre, Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines [Ph.D. thesis], MIT Press, 2006.
  25. A. Hyvärinen, P. O. Hoyer, and M. Inki, “Topographic independent component analysis,” Neural Computation, vol. 13, no. 7, pp. 1527–1558, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. B. A. Olshausen and D. J. Field, “Sparse coding with an overcomplete basis set: a strategy employed by V1?” Vision Research, vol. 37, no. 23, pp. 3311–3325, 1997. View at Publisher · View at Google Scholar · View at Scopus
  27. W. E. Vinje and J. L. Gallant, “Sparse coding and decorrelation in primary visual cortex during natural vision,” Science, vol. 287, no. 5456, pp. 1273–1276, 2000. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Poggio and T. Serre, Models of Visual Cortex, Scholarpedia, 2011.
  29. R. VanRullen and S. J. Thorpe, “Surfing a spike wave down the ventral stream,” Vision Research, vol. 42, no. 23, pp. 2593–2615, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. C. G. Gross, Brain Vision and Memory: Tales in the History of Neuroscience, MIT Press, 1998.
  31. W. Zheng, X. Zhou, C. Zou, and L. Zhao, “Facial expression recognition using kernel canonical correlation analysis (KCCA),” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 233–238, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. A. J. Bell and T. J. Sejnowski, “The 'independent components' of natural scenes are edge filters,” Vision Research, vol. 37, no. 23, pp. 3327–3338, 1997. View at Publisher · View at Google Scholar · View at Scopus
  33. JAFEE dataset, http://www.kasrl.org/jaffe.html.
  34. F. Y. Shih, C. F. Chuang, and P. S. P. Wang, “Performance comparisons of facial expression recognition in JAFFE database,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 22, no. 3, pp. 445–459, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. H. B. Deng, L. W. Jin, L. X. Zhen, and J. C. Huang, “A new facial expression recognition method based on local gabor filter bank and PCA plus LDA,” International Journal of Information Technology, vol. 11, no. 11, pp. 86–96, 2005.
  36. S. Y. Fu, G. S. Yang, and Z. G. Hou, “Multiple kernel learning with ICA: local discriminative image descriptors for recognition,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '10), July 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Tian, T. Kanade, and J. Cohn, “Evaluation of gabor wavelet based facial action unit recognition in image sequences of inceasing complexity,” in Proceedings of the International Conference on Multi-Modal Interface, 2002.
  38. F. Cheng, J. Yu, and H. Xiong, “Facial expression recognition in JAFFE dataset based on Gaussian process classification,” IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1685–1690, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. N. T. Alves, J. A. Aznar-Casanova, and S. S. Fukusima, “Patterns of brain asymmetry in the perception of positive and negative facial expressions,” Laterality, vol. 14, no. 3, pp. 256–272, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. Cohn-Kanada AU-Coded dataset, http://www.pitt.edu/~jeffcohn/CKandCK+.htm.
  41. R. E. Jack, C. Blais, C. Scheepers, P. G. Schyns, and R. Caldara, “Cultural confusions show that facial expressions are not universal,” Current Biology, vol. 19, no. 18, pp. 1543–1548, 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. R. E. Jack, R. Caldara, and P. G. Schyns, “Internal representations reveal cultural diversity in expectations of facial expressions of emotion,” Journal of Experimental Psychology, vol. 141, no. 1, pp. 19–25, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, vol. 381, no. 6583, pp. 607–609, 1996. View at Publisher · View at Google Scholar · View at Scopus
  44. T. O. Sharpee, H. Sugihara, A. V. Kurgansky, S. P. Rebrik, M. P. Stryker, and K. D. Miller, “Adaptive filtering enhances information transmission in visual cortex,” Nature, vol. 439, no. 7079, pp. 936–942, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. C. E. Connor, “A new viewpoint on faces,” Science, vol. 330, no. 6005, pp. 764–765, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. W. A. Freiwald and D. Y. Tsao, “Functional compartmentalization and viewpoint generalization within the macaque face-processing system,” Science, vol. 330, no. 6005, pp. 845–851, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. W. A. Freiwald, D. Y. Tsao, and M. S. Livingstone, “A face feature space in the macaque temporal lobe,” Nature Neuroscience, vol. 12, no. 9, pp. 1187–1196, 2009. View at Publisher · View at Google Scholar · View at Scopus
  48. N. Kasabov, “To spike or not to spike: a probabilistic spiking neuron model,” Neural Networks, vol. 23, no. 1, pp. 16–19, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. A. V. M. Herz, T. Gollisch, C. K. Machens, and D. Jaeger, “Modeling single-neuron dynamics and computations: a balance of detail and abstraction,” Science, vol. 314, no. 5796, pp. 80–85, 2006. View at Publisher · View at Google Scholar · View at Scopus
  50. W. L. Braje, D. Kersten, M. J. Tarr, and N. F. Troje, “Illumination effects in face recognition,” Psychobiology, vol. 26, no. 4, pp. 371–380, 1998. View at Scopus
  51. Y. Yamane, E. T. Carlson, K. C. Bowman, Z. Wang, and C. E. Connor, “A neural code for three-dimensional object shape in macaque inferotemporal cortex,” Nature Neuroscience, vol. 11, no. 11, pp. 1352–1360, 2008. View at Publisher · View at Google Scholar · View at Scopus
  52. Z. U. Rahman, D. J. Jobson, and G. A. Woodell, “Multi-scale retinex for color image enhancement,” in Proceedings of the 1996 IEEE International Conference on Image Processing (ICIP '96), pp. 1003–1006, September 1996. View at Scopus
  53. T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression database,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1615–1618, 2003. View at Publisher · View at Google Scholar · View at Scopus