http://iet.metastore.ingenta.com
1887

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to pre-processing approaches. However, they also require post-processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post-processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.

References

    1. 1)
      • 1. Lai, Z., Wong, W. K., Xu, Y., et al: ‘Approximate orthogonal sparse embedding for dimensionality reduction’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, (4), pp. 723735.
    2. 2)
      • 2. Wang, S., Lu, J., Gu, X., et al: ‘Semi-supervised linear discriminant analysis for dimension reduction and classification’, Pattern Recognit., 2016, 57, pp. 179189.
    3. 3)
      • 3. Yi, S., Lai, Z., He, Z., et al: ‘Joint sparse principal component analysis’, Pattern Recognit., 2017, 61, pp. 524536.
    4. 4)
      • 4. Huang, K. K., Dai, D. Q., Ren, C. X.: ‘Regularized coplanar discriminant analysis for dimensionality reduction’, Pattern Recognit., 2017, 62, pp. 8798.
    5. 5)
      • 5. Turk, M. A., Pentland, A. P.: ‘Face recognition using eigenfaces’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Proc. CVPR'91, Maui, June 1991, pp. 586591.
    6. 6)
      • 6. Liu, X. Z., Zhang, C. G.: ‘Fisher discriminant analysis based on kernel cuboid for face recognition’, Soft Comput., 2016, 20, (3), pp. 831840.
    7. 7)
      • 7. Hyvarinen, A.: ‘Fast and robust fixed-point algorithms for independent component analysis’, IEEE Trans. Neural Netw., 1999, 10, (3), pp. 626634.
    8. 8)
      • 8. Lu, M., Huang, J. Z., Qian, X.: ‘Sparse exponential family principal component analysis’, Pattern Recognit., 2016, 60, pp. 681691.
    9. 9)
      • 9. Xu, Y., Fang, X., Wu, J., et al: ‘Discriminative transfer subspace learning via low-rank and sparse representation’, IEEE Trans. Image Process., 2016, 25, (2), pp. 850863.
    10. 10)
      • 10. Abou-Moustafa, K. T., De La Torre, F., Ferrie, F. P.: ‘Pareto models for discriminative multiclass linear dimensionality reduction’, Pattern Recognit., 2015, 48, (5), pp. 18631877.
    11. 11)
      • 11. Boukabou, W. R., Bouridane, A., Al-Maadeed, S.: ‘Enhancing face recognition using directional filter banks’, Digit. Signal Process., 2013, 23, (2), pp. 586594.
    12. 12)
      • 12. Wang, B., Li, W., Yang, W., et al: ‘Illumination normalization based on weber's law with application to face recognition’, IEEE Signal Process. Lett., 2011, 18, (8), pp. 462465.
    13. 13)
      • 13. Shui, P. L., Zhou, Z. F., Li, J. X.: ‘Image denoising algorithm via best wavelet packet base using Wiener cost function’, IET Image Process., 2007, 1, (3), pp. 311318.
    14. 14)
      • 14. Singh, K., Kapoor, R.: ‘Image enhancement via median-mean based sub-image-clipped histogram equalization’, Optik-Int. J. Light Electron Opt., 2014, 125, (17), pp. 46464651.
    15. 15)
      • 15. Gundimada, S., Asari, V. K., Gudur, N.: ‘Face recognition in multi-sensor images based on a novel modular feature selection technique’, Inf. Fusion, 2010, 11, (2), pp. 124132.
    16. 16)
      • 16. Xu, Y., Fang, X., Li, X., et al: ‘Data uncertainty in face recognition’, IEEE Trans. Cybern., 2014, 44, (10), pp. 19501961.
    17. 17)
      • 17. Xu, Y., Zhu, X., Li, Z., et al: ‘Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition’, Pattern Recognit., 2013, 46, (4), pp. 11511158.
    18. 18)
      • 18. Xu, Y., Zhang, Z., Lu, G., et al: ‘Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification’, Pattern Recognit., 2016, 54, pp. 6882.
    19. 19)
      • 19. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on featured distributions’, Pattern Recognit., 1996, 29, (1), pp. 5159.
    20. 20)
      • 20. Huang, K. K., Dai, D. Q., Ren, C. X., et al: ‘Fusing landmark-based features at kernel level for face recognition’, Pattern Recognit., 2017, 63, pp. 406415.
    21. 21)
      • 21. Wang, N., Li, Q., El-Latif, A. A. A., et al: ‘An enhanced thermal face recognition method based on multiscale complex fusion for Gabor coefficients’, Multimedia Tools Appl., 2014, 72, (3), pp. 23392358.
    22. 22)
      • 22. Liu, F., Tang, Z., Tang, J.: ‘WLBP: Weber local binary pattern for local image description’, Neurocomputing, 2013, 120, pp. 325335.
    23. 23)
      • 23. Li, J., Sang, N., Gao, C.: ‘LEDTD: local edge direction and texture descriptor for face recognition’, Signal Process., Image Commun., 2016, 41, pp. 4045.
    24. 24)
      • 24. Shen, F., Shen, C., Zhou, X., et al: ‘Face image classification by pooling raw features’, Pattern Recognit., 2016, 54, pp. 94103.
    25. 25)
      • 25. Zuo, W., Zhang, H., Zhang, D., et al: ‘Post-processed LDA for face and palmprint recognition: what is the rationale’, Signal Process., 2010, 90, (8), pp. 23442352.
    26. 26)
      • 26. Wang, K., Zuo, W., Zhang, D.: ‘Post-processing on LDA's discriminant vectors for facial feature extraction’, in ‘Audio-and video-based biometric person authentication’ (Springer Berlin, Heidelberg, 2005), pp. 201213.
    27. 27)
      • 27. Abbad, A., Douini, Y., Abbad, K., et al: ‘Post-processing of dimensionality reduction methods for face recognition’, Pattern Recognit. Image Anal., 2017, 27, (2), pp. 266275.
    28. 28)
      • 28. Wu, Z., Huang, N. E., Chen, X.: ‘The multi-dimensional ensemble empirical mode decomposition method’, Adv. Adapt. Data. Anal., 2009, 1, (03), pp. 339372.
    29. 29)
      • 29. Berrani, S. A., Garcia, C.: ‘Robust detection of outliers for projection-based face recognition methods’, Multimedia Tools Appl., 2008, 38, (2), pp. 271291.
    30. 30)
      • 30. Abbad, A., Abbad, K., Tairi, H.: ‘An efficient post-processing approach for dimensionality reduction methods for face recognition’. 2017 Int. Conf. on Advanced Technologies for Signal and Image Processing (ATSIP), Fez, May 2017, pp. 17.
    31. 31)
      • 31. Huang, N. E., Shen, Z., Long, S. R., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’, Philos. Trans. R. Soc. Lond. A, Math. Phys. Eng. Sci., 1998, 454, (1971), pp. 903995.
    32. 32)
      • 32. Wu, Z., Huang, N. E.: ‘Ensemble empirical mode decomposition: a noise-assisted data analysis method’, Adv. Adapt. Data. Anal., 2009, 1, (01), pp. 141.
    33. 33)
      • 33. Yeh, J. R., Shieh, J. S., Huang, N. E.: ‘Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method’, Adv. Adapt. Data. Anal., 2010, 2, (02), pp. 135156.
    34. 34)
      • 34. Van Der Maaten, L.: ‘Accelerating t-SNE using tree-based algorithms’, J. Mach. Learn. Res., 2014, 15, (1), pp. 32213245.
    35. 35)
      • 35. Olivetti & Oracle Research Laboratory: ‘The Olivetti & oracle research laboratory face database of faces’. Available at: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
    36. 36)
      • 36. Samaria, F. S., Harter, A. C.: ‘Parameterisation of a stochastic model for human face identification’. Proc. of the Second IEEE Workshop on Applications of Computer Vision, Sarasota, December 1994, pp. 138142.
    37. 37)
      • 37. Georghiades, A. S., Belhumeur, P. N., Kriegman, D. J.: ‘From few to many: illumination cone models for face recognition under variable lighting and pose’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (6), pp. 643660.
    38. 38)
      • 38. Martinez, A. M.: ‘The AR face database’. CVC technical report, 1998, p. 24.
    39. 39)
      • 39. Huang, G. B., Ramesh, M., Berg, T., et al: ‘Labeled faces in the wild: a database for studying face recognition in unconstrained environments’. Technical Report 07-49, University of Massachusetts, Amherst, 2007, vol. 1, no. 2, p. 3.
    40. 40)
      • 40. Wolf, L., Hassner, T., Taigman, Y.: ‘Similarity scores based on background samples’. Asian Conf. on Computer Vision, Springer Berlin Heidelberg, September 2009, pp. 8897.
    41. 41)
      • 41. Wolf, L., Hassner, T., Maoz, I.: ‘Face recognition in unconstrained videos with matched background similarity’. 2011 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, June 2011, pp. 529534.
    42. 42)
      • 42. Zhu, P., Zhang, L., Hu, Q., et al: ‘Multi-scale patch based collaborative representation for face recognition with margin distribution optimization’. European Conf. on Computer Vision, Springer Berlin Heidelberg, October 2012, pp. 822835.
    43. 43)
      • 43. Gao, G., Yang, J., Wu, S., et al: ‘Bayesian sample steered discriminative regression for biometric image classification’, Appl. Soft Comput., 2015, 37, pp. 4859.
    44. 44)
      • 44. Shen, F., Yang, Y., Zhou, X., et al: ‘Face identification with second-order pooling in single-layer networks’, Neurocomputing, 2016, 187, pp. 1118.
    45. 45)
      • 45. Zou, H., Hastie, T., Tibshirani, R.: ‘Sparse principal component analysis’, J. Comput. Graph. Stat., 2006, 15, (2), pp. 265286.
    46. 46)
      • 46. Clemmensen, L., Hastie, T., Witten, D., et al: ‘Sparse discriminant analysis’, Technometrics., 2011, 53, (4), pp. 406413.
    47. 47)
      • 47. Sharma, A., Paliwal, K. K.: ‘A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices’, Pattern Recognit., 2012, 45, (6), pp. 22052213.
    48. 48)
      • 48. Zhou, Y., Sun, S.: ‘Manifold partition discriminant analysis’, IEEE Trans. Cybern., 2016, 47, (4), pp. 830840.
    49. 49)
      • 49. Yang, M., Zhang, L., Yang, J., et al: ‘Regularized robust coding for face recognition’, IEEE Trans. Image Process., 2013, 22, (5), pp. 17531766.
    50. 50)
      • 50. Mehta, R., Yuan, J., Egiazarian, K.: ‘Face recognition using scale-adaptive directional and textural features’, Pattern Recognit., 2014, 47, (5), pp. 18461858.
    51. 51)
      • 51. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, June 2005, vol. 1, pp. 886893.
    52. 52)
      • 52. Vu, N. S., Caplier, A.: ‘Enhanced patterns of oriented edge magnitudes for face recognition and image matching’, IEEE Trans. Image Process., 2012, 21, (3), pp. 13521365.
    53. 53)
      • 53. Wolf, L., Hassner, T., Taigman, Y.: ‘Effective unconstrained face recognition by combining multiple descriptors and learned background statistics’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (10), pp. 19781990.
    54. 54)
      • 54. Yang, D., Chen, W., Wang, J., et al: ‘Patch based face recognition via fast collaborative representation based classification and expression insensitive two-stage voting’. Int. Conf. on Computational Science and Its Applications, Beijing, July 2016, pp. 562570.
    55. 55)
      • 55. Ma, C., Jung, J. Y., Kim, S. W., et al: ‘Random projection-based partial feature extraction for robust face recognition’, Neurocomputing, 2015, 149, pp. 12321244.
    56. 56)
      • 56. Huang, S., Yang, D., Ge, Y., et al: ‘Discriminant hyper-Laplacian projections and its scalable extension for dimensionality reduction’, Neurocomputing, 2016, 173, pp. 145153.
    57. 57)
      • 57. Ahonen, T., Hadid, A., Pietikainen, M.: ‘Face description with local binary patterns: application to face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (12), pp. 20372041.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5033
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5033
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address