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Improved Combination of LBP plus LFDA for Facial Expression Recognition using SRC

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 13
Year of Publication: 2014
Authors:
Ritesh Bora
V. A. Chakkarwar
10.5120/16857-6731

Ritesh Bora and V A Chakkarwar. Article: Improved Combination of LBP plus LFDA for Facial Expression Recognition using SRC. International Journal of Computer Applications 96(13):38-44, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Ritesh Bora and V. A. Chakkarwar},
	title = {Article: Improved Combination of LBP plus LFDA for Facial Expression Recognition using SRC},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {13},
	pages = {38-44},
	month = {June},
	note = {Full text available}
}

Abstract

Human facial expression is one of the most powerful, natural and immediate means for communication between each other. Automatic human facial expression recognition is challenging, interesting problem in many areas such as human computer interaction and data driven animation etc. In this paper, Facial expression based on Local Binary Pattern (LBP) is evaluated, "curse of dimensionality" for real world scenarios problem solved by dimensionality reduction using Local Fisher Discriminant Analysis (LFDA) and Sparse representation classifier (SRC) used for efficient facial expression classification. The experiment is performed in both person-independent and person-dependent facial expression recognition cases, on Japanese Female Facial Expression (JAFFE) and observed that LBP features perform stably and robustly over useful range of low resolutions of face images (150 by 110 pixel and 64 by 64 pixel size) . Proposed method shows better result than traditional algorithms such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and LBP+SRC solely.

References

  • Ming-wei Huang, Zhe-wei Wang and Zi-Lu Ying, "A New Method For Facial Expression Recognition Based On Sparse Representation Plus LBP", 3rd International Congress on image and Signal Processing, 2010, pp. 1750-1754.
  • Shiqing Zhang, Xiaoming Zhao and Bicheng Lei, "Facial Expression Recognition based on Local Binary Patterns and Local Fisher Discriminant Analysis", WSEAS Transactions on Signal Processing, 2012, issue 1, vol. 8, pp. 21-30.
  • M. Pantic and L. Rothkrantz, "Automatic Analysis of Facial Expressions: The State of the Art", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, 2000, pp. 1424-1445.
  • B. Fasel and J. Luettin, "Automatic Facial Expression Analysis: A Survey", Pattern Recognition, Vol. 36, 2003, pp. 259-275.
  • W. Fellenz, J. Taylor, N. Tsapatsoulis, and S. Kollias, "Comparing Template-based, Feature based and Supervised Classification of Facial Expression from Static Images", Computational Intelligence and Applications, 1999.
  • M. Lyons, J. Budynek, and S. Akamastu, "Automatic Classification of Single Facial Images", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, 1999, pp. 1357-1362.
  • Y. Shinohara and N. Otsu, "Facial Expression Recognition Using Fisher Weight Maps ", IEEE Conf. on Automatic Face and Gesture Recognition, 2004, pp. 499-504.
  • W. Zheng, X. Zhou, C. Zou and L. Zhao, "Facial expression recognition using kernel canonical correlation analysis (KCCA)", IEEE Trans. on Neural Networks, Vol. 17, 2006, pp. 233-238.
  • Z. Zhang, M. Lyons, M. Schuster, and S. Akamatsu, "Comparison Between Geometry based and Gabor-Wavelet-based Facial Expression Recognition Using Multi-layer Perception", Proc. 3rd Int. Conf. Automatic Face and Gesture Recognition, 1998, pp. 454-459.
  • Y. Tian, T. Kanade and J Cohn, "Facial expression analysis, Handbook of face recognition", Springer, October 2003.
  • Z. Zhang, M. J. Lyons, M. Schuster, and S. Akamatsu. "Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perception", IEEE FG, April 1998.
  • Y. Tian, "Evaluation of face resolution for expression analysis",IEEE Workshop on Face Processing in Video, 2004.
  • T. Ojala, M Pietikinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distribution, Pattern Recognition", vol. 29,No. 1, 1996.
  • T Ojala, M Pietik inen, and T M Enp, "Multi resolution gray scale and rotation invariant texture analysis with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence", Vol. 24, No. 7, 2002, pp. 971-987
  • T. Ahonen, A. Hadid, and M. Pietikinen, "Face recognition with local binary patterns", ECCV, 2004, pp. 469-481.
  • A. Hadid, M. Pietikinen, and T. Ahonen, . "A discriminative feature space for detecting and recognizing faces",IEEE CVPR, June 2004, pp. 797-804.
  • P N Belhumeur, J P Hespanha, and D J Kriegman, "Eigen faces vs. fisher faces: Recognition using class specific linear projection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 711-720.
  • M Sugiyama, T Idé, S Nakajima and et al. , "Semi-supervised local Fisher discriminant analysis for dimensionality reduction", Machine learning, Vol. 78, No. 1, 2010, pp. 35-61.
  • X He, and P Niyogi, "Locality preserving projections, Advances in neural information processing systems (NIPS)", MIT Press, 2003.
  • Wright. J, Yang. A. Y, Ganesh. A, Sastry. S. S, and Ma. Y, "Robust Face Recognition via Sparse Representation" , IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, pp. 210-227, Feb. 2009.
  • M A Turk, and A P Pentland, "Face recognition using Eigen faces", Proc. IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
  • Candes. E. J, "Compressive sampling", International Congress of Mathematicians, Aug. 2006.
  • Candes. E. J, Romberg. J, and Tao. T, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information", IEEE Transactions on Information Theory, Vol. 52, No. 2, pp. 489-509, Feb. 2006.
  • Donoho. D. L, "For Most Large Underdetermined Systems of Linear Equations the Minimal 11 -Norm Solution Is Also the Sparest Solution", Communication on Pure and Applied math, Vol. 59, No. 6, pp. 797-829, May. 2006.
  • Candes. E. J, and Tao. T, "Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies", IEEE Transaction on Information Theory, Vol. 52, No. 12, pp. 5406-5426,Dec. 2006.
  • S. Chen, D. Donoho and M. Saunders, "Atomic Decomposition by Basis Pursuit," SIAM Rev. , Vol. 43, No. 1, pp. 129-159, 2001.
  • Donoho. D. L, and Tsaig. Y, "Fast Solution of l1 -Norm Minimization Problems, When the Solution May Be Sparse", http://www. stanford. edu/tsaig/research. html.
  • C Shan, S Gong, and P McOwan, "Robust facial expression recognition using local binary patterns", Proc. IEEE International Conference on Image Processing, 2005, pp. 370-373.
  • C Shan, S Gong, and P McOwan, "Facial expression recognition based on Local Binary Patterns: A comprehensive study, Image and Vision Computing", Vol. 27, No. 6, 2009, pp. 803-816.
  • T Jinghai, Y Zilu, and Z Youwei, "The contrast analysis of facial expression recognition by human and computer", Proc. 8th International Conference on Signal Processing, 2006, pp. 1649-1653.