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Facial Expression Recognition using Patch based Gabor Features

Anju Chandran, Vaqar Ansari. Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Anju Chandran, Vaqar Ansari
10.5120/ijais2016451526
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  1. Anju Chandran and Vaqar Ansari. Article: Facial Expression Recognition using Patch based Gabor Features. International Journal of Applied Information Systems 10(7):23-28, March 2016. BibTeX

    @article{key:article,
    	author = "Anju Chandran and Vaqar Ansari",
    	title = "Article: Facial Expression Recognition using Patch based Gabor Features",
    	journal = "International Journal of Applied Information Systems",
    	year = 2016,
    	volume = 10,
    	number = 7,
    	pages = "23-28",
    	month = "March",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"
    }
    

Abstract

Facial Expression is one of the most natural, and powerful means for human beings to Communicate their emotions and intentions. The recognition of facial expressions is very important for interactive Human Computer Interfaces. One crucial step for facial expression recognition (FER) is the accurate extraction of emotional features. Numerous feature extraction techniques have been developed for recognition of expressions from static images as well as videos. This paper put forward an approach using distance features that are obtained by extracting patch based 3D Gabor features and conducting patch matching operations. The experimental results shows high correct recognition rate (CRR), fast processing time and significant performance improvements because of the consideration of facial components and muscle movements. Comparison with the state -of -the art indicates that the proposed approach achieves high CRR for JAFFE database and is one among the top performers on the Cohn-Kanade (CK) database.

Reference

  1. B. Fasel and J. Luettin, “Automatic Facial Expression Analysis: A Survey,” Pattern Recog., vol. 36, no. 1, (2003), pp. 259-275.
  2. M. Pantic and L. J. M. Rothkrantz, “Automatic Analysis of Facial Expressions: The State of the Art”, IEEE Trans. Pattern Anal.Mach. Intell., vol. 22, no. 12, (2000), pp. 1424-1445.
  3. Pantic.M. and Ioannis Patras. ―Dynamics of Facial Expression: Recognition of Facial Actions and Their Temporal Segments from Face Profile Image Sequences‖, IEEE transactions on Systems, Man, and Cybernetics—Part B: cybernetics, vol. 36, no. 2, 2006.
  4. V. Bruce, "What the Human Face Tells the Human Mind: Some Challenges for the Robot-Human Interface", Proc. IEEE Int. Workshop Robot and Human Communication, pp. 44-51, 1992
  5. G. Donato, M.S. Bartlett, J.C. Hager, P. Ekman, T.J. Sejnowski, "Classifying Facial Actions", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, No. 10, pp. 974-989, 1999
  6. I.A. Essa, A.P. Pentland, "Coding, Analysis, Interpretation, and Recognition of Facial Expressions",
  7. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 757-763, 1997
  8. T. Gritti, C. Shan, V. Jeanne and R. Braspenning, “Local Features based Facial Expression Recognition with Face Registration Errors” 978-1-4244-1/08/2008. IEEE
  9. G. R. S. Murthy and R. S. Jadon, “Effectiveness of Eigenspaces for Facial Expressions Recognition”, International Journal of Computer Theory and Engineering, vol. 1, no. 5, (2009) December.
  10. B. Fasel and J. Luttin, “Automatic Facial Expression Analysis: a survey”, Pattern Recognition, vol. 36, no. 1, (2003), pp. 259-275.
  11. Z. Zhang, “Comparition between Geometry-Based and Gobor-wavelet-based Facial Expression Recognition Using Multi-layer Perception”, Proc. IEEE Int. Conf. Auto. Face Gesture Recog., (1998) April, pp. 454-459
  12. Bouchra Abboud, Franck Davoine, Mo Dang, “Facial expression recognition and synthesis based on an appearance model” 3 May 2004 Elsevier.
  13. Ekman, P, Friesen, “Constants across Cultures in the Face and Emotion”, J. Pers. Psycho. WV, 1971, vol. 17, no. 2, pp. 124-129
  14. Ekman, P, Friesen, “Facial expressions of emotion: an old controversy and new findings,” Philos. Trans. R. Soc. Lond. B, Biol. Sci., vol. 335, pp. 63–69, 1992.
  15. P. Ekman and W.V. Friesen, “Manual for the Facial Action Coding System,” Consulting Psychologists Press, 1977.
  16. L. Sirovich and M. Kirby, “Low Dimensional Procedure for Characterization of Human Faces,” J. Optical Soc. Am., vol. 4, pp. 519-524, 1987.
  17. K. Mase and A. Pentland, “Recognition of facial expression from optical flow,” IEICE Trans. E, vol. 74, pp. 408–410, 1991
  18. I. Essa and A. Pentland, “Facial expression recognition using a dynamic model and motion energy,” presented at the Int. Conf. on Computer Vision, 1995
  19. I. Essa and A. Pentland, “Coding, analysis, interpretation, and recognition of facial expressions,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 757–763, July 1997
  20. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, Jul. 1997.
  21. B.A. Draper, K. Baek, M.S. Bartlett, J.R. Beveridge, “Recognizing Faces with PCA and ICA,” Computer Vision and Image Understanding: special issue on face recognition, in press.
  22. Y. Tian, T. Kanade, and J. Cohn, “Recognizing action units for facial expression analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 2, pp. 97–115, Feb. 2001.
  23. Jyh-Yeong Chang and Jia-Lin Chen, “Automated Facial Expression Recognition System Using Neural Networks” Journal of the Chinese Institute of Engineers, Vol. 24, No. 3, pp. 345-356 (2001).
  24. Hong-Bo Deng, Lian-Wen Jin, Li-Xin Zhen, Jian-Cheng 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 2005
  25. Ceifeng Shan, et al., “Facial expression recognition based on Local Binary Patterns: A comprehensive Study”, Elsevier Science Ltd Trans., 2009.
  26. S. Luiz OLiveira, et al., “2D Principal Component Analysis for face and facial expression recognition”, IEEE Trans., 2011.
  27. D. Gabor, “Theory of Communication,” J. Institution of Electrical Engineers—Part III: Radio and Comm. Eng., vol. 93, pp. 429-441, 1946.
  28. C.C. Chang and C.J. Lin, “LIBSVM: A Library for Support Vector Machines, 2001,” http://www.csie.ntu.edu.tw/cjlin/libsvm,2001.
  29. A. Farahat, A. Ghodsi, and M. Kamel, “An efficient greedy method for unsupervised feature selection,” in
  30. Data Mining (ICDM), 2011 IEEE 11th International Conference on, dec. 2011, pp. 161 –170.
  31. C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, pp. 273-297, 1995.
  32. http://www.kasrl.org/jaffe.html.
  33. T. Kanade, J.F. Cohn, and T. Yingli, “Comprehensive Database for Facial Expression Analysis,” Proc. IEEE Fourth Int’l Conf. Automatic Face and Gesture Recognition, pp. 46-53, 2000.
  34. W. Yuwen, L. Hong, and Z. Hongbin, “Modeling Facial Expression Space for Recognition,” Proc. IEEE/RSJ Int’l Conf. Intelligent Robots and Systems, pp. 1968-1973, 2005.
  35. G. Guo and C.R. Dyer, “Learning from Examples in the Small Sample Case: Face Expression Recognition,” IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 35, no. 3, pp. 477-488, June 2005.
  36. G. Littlewort, M.S. Bartlett, I. Fasel, J. Susskind, and J. Movellan, “Dynamics of Facial Expression Extracted Automatically from Video,” Image and Vision Computing, vol. 24, pp. 615-625, 2006
  37. Z. Wenming, Z. Xiaoyan, Z. Cairong, and Z. Li, “Facial Expression Recognition Using Kernel Canonical Correlation Analysis (KCCA),” IEEE Trans. Neural Networks, vol. 17, no. 1, pp. 233- 238, Jan. 2006.
  38. H.Y. Chen, C.L. Huang, and C.M. Fu, “Hybrid-Boost Learning for Multi-Pose Face Detection and Facial Expression Recognition,” Pattern Recognition, vol. 41, pp. 1173-1185, 2008.
  39. J. Bin, Y. Guo-Sheng, and Z. Huan-Long, “Comparative Study of Dimension Reduction and Recognition Algorithms of DCT and 2DPCA,” Proc. Int’l Conf. Machine Learning and Cybernetics, pp. 407- 410, 2008.
  40. C. Shan, S. Gong, and P.W. McOwan, “Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study,” Image and Vision Computing, vol. 27, pp. 803-816, 2009.
  41. J.-J. Wong and S.-Y. Cho, “A Face Emotion Tree Structure Representation with Probabilistic Recursive Neural Network Modeling,” Neural Computing and Applications, vol. 19, pp. 33-54, 2010.

Keywords

Facial components, feature extraction, Gabor filter, patch matching.