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Human facial expression analysis based on image granule LPP

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Abstract

This paper proposes a synthetic technique for human facial expression analysis based on locality preserving projections (LPP) and granular computing. The proposed method decreases the computational complexity of LPP and preserves the performance of LPP algorithm. In image processing, an image can be divided into various sizes of blocks, which are defined as image granules in this paper. The LPP algorithm is implemented to images with various image granules. By this strategy, the image dimension reduces quickly, which decreases the computational complexity. The experiments on three face databases are presented to show the performance of image granule LPP. Distribution of the facial pose and expression is shown and the computational complexities with different image granules are compared. Meanwhile, the order-preserving property of images is investigated by tracking the sequence of designated images. And the loss of image information is analyzed by image roughness, entropy and histogram. Furthermore, the parameter setting in LPP is discussed because of its non-ignorable affect on the experiment results. Finally, the method is applied to facial expression recognition.

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References

  1. Li SZ, Jain AK (2005) Handbook of face recognition. Springer, New York

    MATH  Google Scholar 

  2. Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Maui, HI, USA, pp 586–591

  3. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  4. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  5. Vishwakarma VP (2013) Illumination normalization using fuzzy filter in DCT domain for face recognition. Int J Mach Learn Cybern. doi:10.1007/s13042-013-0182-4

  6. Li Y, Liu X, Gao Z (2013) Shadow determination and compensation for face recognition. Int J Mach Learn Cybern. doi:10.1007/s13042-013-0207-z

  7. Poon B, Amin MA, Yan H (2011) Performance evaluation and comparison of PCA based human face recognition methods for distorted images. Int J Mach Learn Cybern 2(4):245–259

    Article  Google Scholar 

  8. Zhao H, Yao L (2013) Face hallucination using example-based regularization. Int J Mach Learn Cybern 4(6):693–701

    Article  MathSciNet  Google Scholar 

  9. Xu X, Liu W, Venkatesh S (2012) An innovative face image enhancement based on principle component analysis[J]. Int J Mach Learn Cybern 3(4):259–267

    Article  Google Scholar 

  10. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  11. Sam Roweis LS, Hinton G (2001) Global coordination of local linear models. Adv Neural Inf Process Syst 14:889–896

    Google Scholar 

  12. Tenenbaum J, de Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(12):2319–2323

    Article  Google Scholar 

  13. Chang Y, Hu C, Turk M (2003) Manifold of facial expression. In: Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, Nice, France

  14. Lee K-C, Ho J, Yang M-H, Kriegman D (2003) Video-based face recognition using probabilistic appearance manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition

  15. Seung HS, Lee DD (2000) The manifold ways of perception. Science 290:2268–2269

    Google Scholar 

  16. Shashua A, Levin A, Avidan S (2002) Manifold pursuit: a new approach to appearance based recognition. In: Proceedings of 16th International Conference on Pattern Recognition 3, pp 590–594

  17. Bernstein M, De Silva V, Langford JC, Tenenbaum JB (2000) Graph approximations to geodesics on embedded manifolds, Technical report, Department of Psychology, Stanford University

  18. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  19. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol 14, pp 589–591

  20. He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of the Conference on Neural Information Processing Systems. Vancouver, Canada, pp 153–160

  21. He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  22. Chung FRK (1997) Spectral graph theory. Regional conference series in mathematics, vol 92

  23. Yan S, Xu D, Zhang B, Zhang H (2005) Graph embedding: a general framework for dimensionality reduction. CVPR 2:830–837

    Google Scholar 

  24. Yang J, Zhang D, Yang JY, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664

    Article  Google Scholar 

  25. He X, Yan S, Hu Y, Zhang H (2003) Learning a locality preserving subspace for visual recognition. In: ICCV 2003, pp 385–393

  26. Lin TY (1997) Granular computing: from rough sets and neighborhood system to information granulation and computing in words. In: European Congress on Intelligent Techniques and Soft Computing

  27. Butenkov SA (2004) Granular computing in image processing and understanding. In: Proceedings of IASTED International Conference on Artificial Intelligence and Applications. Innsbruck, Austria

  28. Pal SK, Uma Shankar B, Mitra P (2005) Granular computing, rough entropy and object extraction. Pattern Recogn Lett 26(16):2509–2517

    Article  Google Scholar 

  29. Butenkov SA, Krivsha VV, Saud AD (2006) Granular computing in computer image perception: basic issues and glass box models. In: Proceedings of the 24th IASTED International Conference on Artificial Intelligence and Applications, pp 462–467

  30. Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16:320–330

    Article  Google Scholar 

  31. Bhatt HS, Samarth B, Singh R, Vatsa M, Noore A (2011) Evolutionary granular approach for recognizing faces altered due to plastic surgery. In ‘FG’, IEEE, pp 720–725

  32. Chung FRK (1997) Spectral graph theory, volume 92 of Regional Conference Series in Mathematics

  33. Skočal D, Bischof H, Leonardis A (2002) A robust PCA algorithm for building representations from panoramic images. European Conference on Computer Vision, vol IV. Springer, New York, pp 761–775

    Google Scholar 

  34. Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MATH  MathSciNet  Google Scholar 

  35. Zadeh LA (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput 2(1):23–25

    Article  Google Scholar 

  36. Lin TY (1998) Granular computing on binary relations in data mining and neighborhood systems. In: Proceedings of Rough Sets in Knowledge Discovery, Physica-Verlag, Heidelberg, pp 107–120

  37. Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 99:1–13

    Google Scholar 

  38. Zhang B, Zhang L (2010) Discuss on future development of granular computing. J Chongqing Univ Posts Telecommun (Natural Science Edition) 22(5):538–540

    Google Scholar 

  39. Zhou G (2006) Granular computing model and its application. Master Degree Dissertation, Zhejiang Normal University

  40. Tian Y, Kanade T, Cohn JF (2005) Facial expression recognition. In: Li SZ, Jain AK (eds) Handbook of face recognition. Springer, New York

  41. Shan C, Gong S, McOwan PW (2006) A comprehensive empirical study on linear subspace methods for facial expression analysis. In: Conference on Vision and Pattern Recognition Workshop (CVPRW ’06)

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Acknowledgments

The authors would like to thank the anonymous reviewers for suggesting many ways to improve the paper. The work is partially supported by the National Natural Science Foundation of China (No. 61170121).

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, China

    Xiuxiu Xu, Jiuzhen Liang, Sisi Lv & Qin Wu

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  1. Xiuxiu Xu
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  2. Jiuzhen Liang
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Correspondence to Jiuzhen Liang.

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Xu, X., Liang, J., Lv, S. et al. Human facial expression analysis based on image granule LPP. Int. J. Mach. Learn. & Cyber. 5, 907–921 (2014). https://doi.org/10.1007/s13042-014-0228-2

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  • DOI: https://doi.org/10.1007/s13042-014-0228-2

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