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Non-linear dimensionality reduction using fuzzy lattices

Non-linear dimensionality reduction using fuzzy lattices

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The proposed method is based on extraction of non-linearity from the nearest neighbourhood elements of image. To detect non-linearity, relation between the nearest neighbourhood elements of the image, have been expressed in terms of Gaussian membership functions. All the elements of the image are connected with the nearest neighbourhood elements with some membership degree of the Gaussian functions. It results in the formation of number of fuzzy lattices. The lattices have been expressed in the form of Schrödinger equation, to find the kinetic energy (KE) used, corresponding to change occurring in the facial activity of a person. Finally, the KE embedded in three dimension space is used to distinguish non-linear changes during occurrence of various facial activities. Experimental results show that proposed method is effective in recognition of facial expression as it focuses on extracting the non-linear features corresponding to contours of maximum energy which are appearing because of different expressions.

References

    1. 1)
      • 1. Tenenbaum, J.B., Silva, V. de, Langford, J.C.: ‘A global geometric framework for nonlinear dimensionality reduction’, Science, 2000, 290, (5500), pp. 23192323 (doi: 10.1126/science.290.5500.2319).
    2. 2)
      • 2. Lee, M.D.: ‘Determining the dimensionality of multidimensional scaling models for cognitive modeling’, J. Math. Psychol., 2001, 45, (1), pp. 149166 (doi: 10.1006/jmps.1999.1300).
    3. 3)
      • 3. Roweis, S.T., Saul, L.K.: ‘Nonlinear dimensionality reduction by locally linear embedding’, Science, 2000, 290, (5500), pp. 23232326 (doi: 10.1126/science.290.5500.2323).
    4. 4)
      • 4. Huang, M.W., Wang, Z.W., Ying, Z.L.A.: ‘Novel method of facial expression recognition based on GPLVM plus SVM’. Proc. Int. Conf. Signal Processing, Beijing, October 2010, pp. 916919.
    5. 5)
      • 5. Cai, D., He, X.F., Han, J.W., Zhang, H.J.: ‘Orthogonal Laplacian faces for face recognition’, IEEE Trans. Image Process., 2006, 15, (11), pp. 36083614 (doi: 10.1109/TIP.2006.881945).
    6. 6)
      • 6. Dacheng Tao, S.S., Chan, K.P.: ‘Evolutionary cross-domain discriminative Hessian eigenmaps’, IEEE Trans. Image Process., 2010, 19, (4), pp. 10751086 (doi: 10.1109/TIP.2009.2035867).
    7. 7)
      • 7. Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: ‘Coding facial expressions with Gabor wavelets’. IEEE Int. Conf. on Automatic Face and Gesture Recognition, Nara, Japan, April 1998, pp. 200205.
    8. 8)
      • 8. Feng, X., Pietikäinen, M., Hadid, A.: ‘Facial expression recognition with local binary patterns and linear programming’, Pattern Recognit. Image Anal., 2005, 15, (2), pp. 546548.
    9. 9)
      • 9. Shan, C., Gong, S., McOwan, P.W.: ‘Facial expression recognition based on local binary patterns: a comprehensive study’, Image Vis. Comput., 2009, 27, (6), pp. 803816 (doi: 10.1016/j.imavis.2008.08.005).
    10. 10)
      • 10. Yeh, Y.R., Huang, S.Y., Lee, Y.J.: ‘Nonlinear dimension reduction with Kernel sliced inverse regression’, IEEE Trans. Knowl. Data Eng., 2009, 21, (11), pp. 15901603 (doi: 10.1109/TKDE.2008.232).
    11. 11)
      • 11. Khurd, P., Davatzikos, C.: ‘On analyzing diffusion tensor images by identifying manifold structure using isomaps’, IEEE Trans. Med. Imaging, 2007, 26, (6), pp. 772778 (doi: 10.1109/TMI.2006.891484).
    12. 12)
      • 12. Qinggang, W., Jianwei, L.: ‘Combining local and global information for nonlinear dimensionality reduction’, J. Neurocomput., 2009, 72, (10–12), pp. 22352241 (doi: 10.1016/j.neucom.2009.01.006).
    13. 13)
      • 13. Vizireanu, D.N., Udrea, R.M.: ‘Visual-oriented morphological foreground content grayscale frames interpolation method’, J. Electron. Imaging, 2009, 18, (2), pp. 13 (doi: 10.1117/1.3134142).
    14. 14)
      • 14. Udrea, R.M., Vizireanu, D.N.: ‘Iterative generalization of morphological skeleton’, J. Electron. Imaging, 2007, 16, (1), pp. 13 (doi: 10.1117/1.2713739).
    15. 15)
      • 15. He, Li., Buenaposada, J.M., Baumela, L.: ‘An empirical comparison of graph-based dimensionality reduction algorithms on facial expressions recognition tasks’. Int. Conf. on Pattern Recognition, Tampa, Florida, December 2008, pp. 14.
    16. 16)
      • 16. Tian, Y., Kanade, T., Cohn, J.F.: ‘Recognizing action units for facial expression analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, pp. 97115 (doi: 10.1109/34.908962).
    17. 17)
      • 17. Buciu, I., Kotropoulos, C., Pitas, I.: ‘ICA and Gabor representation for facial expression recognition’, Proc. IEEE Int. Conf. on Image Process., 2003, 2, (3), pp. 855858.
    18. 18)
      • 18. Yang, J., Zhang, D., Frangi, A., Yang, J.: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 131137 (doi: 10.1109/TPAMI.2004.1261097).
    19. 19)
      • 19. Kong, H., Teoh, E., Wang, J., Venkateswarlu, R.: ‘Two dimensional Fisher discriminant analysis: forget about small sample size problem’. Proc. IEEE Conf. on Acoustics Speech Signal Processing, March 2005, pp. 761764.
    20. 20)
      • 20. Kapoor, R., Gupta, R.: ‘Fuzzy Lattice based technique for classification of power quality disturbancesEuropean Trans. Electrical Power, (Wiley online library, 2011) DOI: 10.1002/etep.624.
    21. 21)
      • 21. Edmonds, E.A.: ‘Lattice Fuzzy Logics’, Int. J. Man-Mach. Stud., 1980, 13, (4), pp. 455465 (doi: 10.1016/S0020-7373(80)80006-X).
    22. 22)
      • 22. Petridis, V., Kaburlasos, V.G.: ‘Learning in the framework of fuzzy lattices’, IEEE Trans. Fuzzy Syst., 1999, 7, (4), pp. 422440 (doi: 10.1109/91.784201).
    23. 23)
      • 23. Kotsia, I., Pitas, I.: ‘Facial expression recognition in image sequences using geometric deformation features and support vector machines’, IEEE Trans. Image Process., 2007, 16, (1), pp. 172187 (doi: 10.1109/TIP.2006.884954).
    24. 24)
      • 24. Guiling, Li., Zhao, Hui.: ‘Facial expression recognition based on the second feature selection and support vector machines’, Comput. Knowl. Technol., 2008, 1, pp. 27002702.
    25. 25)
      • 25. Kanade, T., Cohn, J., Tian, Y.: ‘Comprehensive database for facial expression analysis’. Proc. IEEE Int. Conf. on Face and Gesture Recognition, 2000, pp. 4653.
    26. 26)
      • 26. http://vasc.ri.cmu.edu//idb/html/face/facial_expression/index.html.
    27. 27)
      • 27. http://www.kasrl.org/jaffe.html.
    28. 28)
      • 28. Martinez, A.M., Benavente, R.: ‘The AR Face Database’. CVC Technical Report #24, June1998.
    29. 29)
      • 29. Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: ‘Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark’. IEEE Int. Conf. on Computer Vision, Workshop BEFIT, Barcelona, Spain, November 2011, pp. 613.
    30. 30)
      • 30. Zhu, X., Ramanan, D.: ‘Face detection, pose estimation and landmark localization in the wild’. Computer Vision and Pattern Recognition, Providence, Rhode Island, June 2012, pp. 18.
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