Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Within-component and between-component multi-kernel discriminating correlation analysis for colour face recognition

  • Author(s): Qian Liu 1, 2  and  Chao Wang 1, 2
    • View affiliations
  • Source: Volume 11, Issue 8, December 2017, p. 663 – 674
    DOI:  10.1049/iet-cvi.2016.0294 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 18/08/2016, Accepted 27/04/2017, Revised 13/01/2017, Published 03/05/2017

The key problem of colour face recognition technique is how to take full advantage of the colour information and extract effective discriminating features. To solve this problem, the authors propose a novel non-linear feature extraction approach for colour face recognition, named dual multi-kernel discriminating correlation analysis, which separately maps different colour components of face images into different non-linear kernel spaces, and then implements multi-kernel learning and discriminant analysis with the correlation metric not only within each colour component but also between diverse components. Then, to choose the optimum kernel space for each colour component and select the most suitable colour space for their approach, they design a kernel selection strategy and a colour space selection strategy, respectively. Experimental results in the face recognition grand challenge version 2 and labelled faces in the wilds databases validate the effectiveness of the proposed approach and two strategies.

Inspec keywords: feature extraction; face recognition; image colour analysis; learning (artificial intelligence)

Other keywords: colour face recognition technique; kernel selection strategy; novel nonlinear feature extraction approach; between-component; dual multikernel discriminating correlation analysis; within-component; colour space selection strategy; optimum kernel space

Subjects: Image recognition; Knowledge engineering techniques; Computer vision and image processing techniques

References

    1. 1)
      • 26. Taigman, Y., Yang, M., Ranzato, M., et al: ‘DeepFace: closing the gap to human-level performance in face verification’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, OH, USA, June 2014, pp. 1701–1708.
    2. 2)
      • 18. Lu, Z., Jiang, X.D., Kot, A.C.: ‘A color channel fusion approach for face recognition’, IEEE Signal Process. Lett., 2015, 22, (11), pp. 1839–1843.
    3. 3)
      • 5. Yang, J., Liu, C.: ‘Color image discriminant models and algorithms for face recognition’, IEEE Trans. Neural Netw., 2008, 19, (12), pp. 2088–2098.
    4. 4)
      • 9. Shin, W.J., Lee, S.H., Min, H.S., et al: ‘Face verification using color sparse representation’. Proc. Int. Workshop on Digital Forensics and Watermarking, Shanghai, China, October 2012 (LNCS, 7809), pp. 290–299.
    5. 5)
      • 17. Xiang, X.G., Yang, J., Chen, Q.P.: ‘Color face recognition by PCA-like approach’, Neurocomputing, 2015, 152, pp. 231–235.
    6. 6)
      • 8. Wang, S., Yang, J., Sun, M., et al: ‘Sparse tensor discriminant color space for face verification’, IEEE Trans. Neural Netw. Learn. Syst., 2012, 23, (6), pp. 876–888.
    7. 7)
      • 31. Lin, Y.Y., Liu, T.L., Fuh, C.S.: ‘Multiple kernel learning for dimensionality reduction’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (6), pp. 1147–1160.
    8. 8)
      • 24. Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: ‘Color local texture features for color face recognition’, IEEE Trans. Image Process., 2012, 21, (3), pp. 1366–1380.
    9. 9)
      • 42. Andrew, G., Arora, R., Bilmes, J., et al: ‘Deep canonical correlation analysis’. Proc. Int. Conf. Machine Learning, Atlanta, GA, USA, June 2013, pp. 1247–1255.
    10. 10)
      • 11. Zhao, C.R., Miao, D.Q., Lai, Z.H., et al: ‘Two-dimensional color uncorrelated discriminant analysis for face recognition’, Neurocomputing, 2013, 113, pp. 251–261.
    11. 11)
      • 3. Niese, R., Al-Hamadi, A., Farag, A., et al: ‘Facial expression recognition based on geometric and optical flow features in colour image sequences’, IET Comput. Vis., 2012, 6, (2), pp. 79–89.
    12. 12)
      • 10. Liu, C.: ‘Effective use of color information for large scale face verification’, Neurocomputing, 2013, 101, pp. 43–51.
    13. 13)
      • 32. Yan, F., Kittler, J., Mikolajczyk, K., et al: ‘Non-sparse multiple kernel Fisher discriminant analysis’, J. Mach. Learn. Res., 2012, 13, (1), pp. 607–642.
    14. 14)
      • 44. Luo, Y., Tao, D.C., Ramamohanarao, K., et al: ‘Tensor canonical correlation analysis for multi-view dimension reduction’, IEEE Trans. Knowl. Data Eng., 2015, 27, (11), pp. 3111–3124.
    15. 15)
      • 40. Fu, Y., Yan, S., Huang, T.S.: ‘Correlation metric for generalized feature extraction’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (12), pp. 2229–2235.
    16. 16)
      • 46. Taylor, J.S., Cristianini, N.: ‘Kernel methods for pattern analysis’ (Cambridge University Press, 2004).
    17. 17)
      • 16. Liu, Q.: ‘Colour-feature dual discriminating correlation analysis for face recognition’, IET Comput. Vis., 2015, 9, (4), pp. 467–475.
    18. 18)
      • 6. Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: ‘A comparative study of preprocessing mismatch effects in color image based face recognition’, Pattern Recognit., 2011, 44, (2), pp. 412–430.
    19. 19)
      • 14. Sun, Y.F., Jia, H.J., Hu, Y.L., et al: ‘Color face recognition based on color image correlation similarity discriminant model’, Multimedia Tools Appl., 2014, 73, (3), pp. 2063–2079.
    20. 20)
      • 7. Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: ‘Boosting color feature selection for color face recognition’, IEEE Trans. Image Process., 2011, 20, (5), pp. 1425–1434.
    21. 21)
      • 12. Huang, S.C., Chen, J., Luo, Z.: ‘Sparse tensor CCA for color face recognition’, Neural Comput. Appl., 2014, 24, (7–8), pp. 1647–1658.
    22. 22)
      • 41. Chen, X.H., Chen, S.C., Xue, H.: ‘Large correlation analysis’, Appl. Math. Comput., 2011, 217, (22), pp. 9041–9052.
    23. 23)
      • 47. Liu, C., Wechsler, H.: ‘Robust coding schemes for indexing and retrieval from large face databases’, IEEE Trans. Image Process., 2000, 9, (1), pp. 132–137.
    24. 24)
      • 37. Kittler, J., Li, Y.P., Matas, J.: ‘On matching scores for LDA based face verification’. Proc. British Machine Vision Conf., Bristol, UK, September 2000, pp. 42–51.
    25. 25)
      • 38. Hardoon, D.R., Szedmak, S., Taylor, J.S.: ‘Canonical correlation analysis: an overview with application to learning method’, Neural Comput., 2004, 16, (12), pp. 2639–2664.
    26. 26)
      • 35. Nazarpour, A., Adibi, P.: ‘Two-stage multiple kernel learning for supervised dimensionality reduction’, Pattern Recognit., 2015, 48, (5), pp. 1854–1862.
    27. 27)
      • 23. Lajevardi, S.M., Wu, H.R.: ‘Facial expression recognition in perceptual color space’, IEEE Trans. Image Process., 2012, 21, (8), pp. 3721–3733.
    28. 28)
      • 20. Wu, F., Jing, X.Y., Dong, X.W., et al: ‘Uncorrelated multi-set feature learning for color face recognition’, Pattern Recognit., 2016, 60, pp. 630–646.
    29. 29)
      • 27. Joshi, A., Gangwar, A.K.: ‘Color local phase quantization (CLPQ) – a new face representation approach using color texture cues’. Proc. Int. Conf. Biometrics, Phuket, Thailand, May 2015, pp. 177–184.
    30. 30)
      • 36. Wang, Q.W., Gu, Y.F., Tuia, D.: ‘Discriminative multiple kernel learning for hyperspectral image classification’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (7), pp. 3912–3927.
    31. 31)
      • 4. Rajapakse, M., Tan, J., Rajapakse, J.: ‘Color channel encoding with NMF for face recognition’. Proc. Int. Conf. Image Processing, Singapore, October 2004, vol. 3, pp. 2007–2010.
    32. 32)
      • 34. Liu, X.Z., Yang, G.: ‘Distributed face recognition using multiple kernel discriminant analysis in wireless sensor networks’, Int. J. Distrib. Sens. Netw., 2014, 2014, (242105), pp. 1–7.
    33. 33)
      • 25. Elbouz, M., Alfalou, A., Brosseau, C., et al: ‘Correlation-based efficient face recognition and color change detection’, Opt. Commun., 2013, 311, (2), pp. 186–200.
    34. 34)
      • 30. Yu, H., Yang, J.: ‘A direct LDA algorithm for high-dimensional data – with application to face recognition’, Pattern Recognit., 2001, 34, (10), pp. 2067–2070.
    35. 35)
      • 45. Guo, Y.W., Ding, X.Q., Liu, C.S., et al: ‘Sufficient canonical correlation analysis’, IEEE Trans. Image Process., 2016, 25, (6), pp. 2610–2619.
    36. 36)
      • 33. Wang, Z., Jie, W.B., Gao, D.Q.: ‘A novel multiple Nyström-approximating kernel discriminant analysis’, Neurocomputing, 2013, 119, pp. 385–395.
    37. 37)
      • 39. Ma, Y., Lao, S., Takikawa, E., et al: ‘Discriminant analysis in correlation similarity measure space’. Proc. Int. Conf. Machine Learning, Corvallis, OR, USA, June 2007, pp. 577–584.
    38. 38)
      • 22. Liu, Z., Liu, C.: ‘Robust face recognition using color information’. Proc. Int. Conf. Biometrics, Alghero, Italy, June 2009 (LNCS, 5558), pp. 122–131.
    39. 39)
      • 13. Wu, S.: ‘Quaternion-based improved LPP method for color face recognition’, Optik, 2014, 125, (10), pp. 2344–2349.
    40. 40)
      • 28. Phillips, P.J., Flynn, P.J., Scruggs, T., et al: ‘Overview of the face recognition grand challenge’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, USA, June 2005, pp. 947–954.
    41. 41)
      • 19. Zou, C.M., Kou, K.I., Wang, Y.L.: ‘Quaternion collaborative and sparse representation with application to color face recognition’, IEEE Trans. Image Process., 2016, 25, (7), pp. 3287–3302.
    42. 42)
      • 29. Huang, G.B., Mattar, M., Lee, H., et al: ‘Learning to align from scratch’. Proc. Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, December 2012, pp. 764–772.
    43. 43)
      • 1. Torres, L., Reutter, J.Y., Lorente, L.: ‘The importance of the color information in face recognition’. Proc. Int. Conf. Image Processing, Kobe, Japan, October 1999, pp. 627–631.
    44. 44)
      • 21. Liu, Z., Liu, C.: ‘Fusion of the complementary discrete cosine features in the YIQ color space for face recognition’, Comput. Vis. Image Underst., 2008, 111, (3), pp. 249–262.
    45. 45)
      • 2. Shih, P., Liu, C.: ‘Improving the face recognition grand challenge baseline performance using color configurations across color spaces’. Proc. Int. Conf. Image Processing, Atlanta, USA, October 2006, pp. 1001–1004.
    46. 46)
      • 15. Liu, Q.: ‘Within-component and between-component discriminant analysis for color face recognition’, Optik, 2014, 125, (21), pp. 6366–6374.
    47. 47)
      • 43. Wang, Y.Q., Li, P., Yao, C.: ‘Hypergraph canonical correlation analysis for multi-label classification’, Signal Process., 2014, 105, pp. 258–267.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0294
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0294
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address