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Colour face recognition using fuzzy quaternion-based discriminant analysis

  • Shuzhe Bao
  • Xiaoning SongEmail author
  • Guosheng Hu
  • Xibei Yang
  • Chunli Wang
Original Article
  • 155 Downloads

Abstract

Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method.

Keywords

Quaternion-based vector Fuzzy parameterized discriminant analysis Colour image recognition 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their constructive suggestions. This work was funded by the National Key Research and Development Program of China (2016YFD0401204), the National Science and Technology Support Program of China (No. 2015 BAD17B02), the Natural Science Foundation of Jiangsu Province (Grants no. BK20161135), China Postdoctoral Science Foundation (Grant no. 2016M590407), the Fundamental Research Funds for the Central Universities (Grant no. JUSRP115A29, JUSRP51618B), the Open Project Program of the Key Laboratory of Intelligent Perception, Systems for High-Dimensional Information of the Ministry of Education (No. JYB201603) and the Open Project Program of the Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University, No. MJUKF201709).

References

  1. 1.
    Yang J, Liu CJ, Zhang L (2010) Color space normalization: enhancing the discriminating power of color spaces for face recognition. Pattern Recognit 43:1454–1466zbMATHGoogle Scholar
  2. 2.
    Liu CJ (2011) Extracting discriminative color features for face recognition. Pattern Recognit Lett 32:1796–1804Google Scholar
  3. 3.
    Zhao CR, Miao DQ, Lai ZH, Gao C, Liu CC, Yang JY (2013) Two-dimensional color uncorrelated discriminant analysis for face recognition. Neurocomputing 113:251–261Google Scholar
  4. 4.
    Luo J, Crandall D (2006) Color object detection using spatial-color joint probability functions. IEEE Trans Image Process 15(6):1443–1453Google Scholar
  5. 5.
    Gevers T, Stokman H (2004) Robust histogram construction from color invariants for object recognition. IEEE Trans Pattern Anal Mach Intell 26(1):113–118Google Scholar
  6. 6.
    Diplaros A, Gevers T, Patras I (2006) Combining color and shape information for illumination-viewpoint invariant object recognition. IEEE Trans Image Process 15(1):1–11Google Scholar
  7. 7.
    Dong G, Xie M (2005) Color clustering and learning for image segmentation based on neural networks. IEEE Trans Neural Netw 16(4):925–936Google Scholar
  8. 8.
    Stokman H, Gevers T (2007) Selection and fusion of color models for image feature detection. IEEE Trans Pattern Anal Mach Intell 29(3):371–381Google Scholar
  9. 9.
    Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 20(3):21–36Google Scholar
  10. 10.
    Yang J, Wright J, Huang T, Ma Y, Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp 1–8Google Scholar
  11. 11.
    Xiong LZ, Xu ZQ, Shi YQ (2017) An integer wavelet transform based scheme for reversible data hiding in encrypted images. Multidimens Syst Signal Process. doi: 10.1007/s11045-017-0497-5 Google Scholar
  12. 12.
    Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11):2594–2608Google Scholar
  13. 13.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518Google Scholar
  14. 14.
    Zheng Y, Jeon B, Xu D, Wu Q, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar
  15. 15.
    Pan Z, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast motion estimation based on content property for low-complexity h. 265/hevc encoder. IEEE Trans Broadcast 62(3):675–684Google Scholar
  16. 16.
    Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176Google Scholar
  17. 17.
    Pan Z, Jin P, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast reference frame selection based on content similarity for low complexity hevc encoder. J Visual Commun Image Represent 40:516–524Google Scholar
  18. 18.
    Feng Z-H, Huber P, Kittler J, Christmas W, Wu X-J (2015) Random cascaded-regression copse for robust facial landmark detection. IEEE Signal Process Lett 1(22):76–80Google Scholar
  19. 19.
    Evangelidis GD, Psarakis EZ (2008) Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans Pattern Anal Mach Intell 30(10):1858–1865Google Scholar
  20. 20.
    Wright A.Wagner, J., Ganesh A, Zhou Z, Mobahi H, Ma Y (2012) Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386Google Scholar
  21. 21.
    Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux JL (2015) Color image analysis by quaternion-type moments. J Math Imaging Vision 51(1):124–144MathSciNetzbMATHGoogle Scholar
  22. 22.
    Mohideen SK, Perumal SA, Sathik MM (2008) Image de-noising using discrete wavelet transform. Int J Comput Sci Netw Secur 8(1):213–216Google Scholar
  23. 23.
    Dong W, Li X, Zhang D, Shi G (2011) Sparsity-based image denoising via dictionary learning and structural clustering. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp 457–464Google Scholar
  24. 24.
    Wang J, Li T, Shi Y-Q, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl. doi: 10.1007/s11042-016-4153-0 Google Scholar
  25. 25.
    Zhou Z, Wang Y, Wu QJ, Yang C-N, Sun X (2016) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12(1):48–63Google Scholar
  26. 26.
    Zhili Z, Ching-Nung Y, Xingming S, Qi L, WU QJ (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans Inform Syst 99(6):1531–1540Google Scholar
  27. 27.
    Chen Y, Hao C, Wu W, Wu E (2016) Robust dense reconstruction by range merging based on confidence estimation. Sci China Inform Sci 59(9):092103Google Scholar
  28. 28.
    Wang Xizhao, Xing Hong-Jie, Li Yan et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654Google Scholar
  29. 29.
    Cheng Y, Jin Z, Chen H (2016) A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation. Int J Mach Learn Cybern 7(1):47–52Google Scholar
  30. 30.
    Nouyed I, Poon B, Amin MA (2016) A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition. Int J Mach Learn Cybern 7(6):1115–1130Google Scholar
  31. 31.
    Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst. doi: 10.1109/TNNLS.2016.2544779 Google Scholar
  32. 32.
    Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale lpq and pca. China Commun 13(7):60–65Google Scholar
  33. 33.
    Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2016) A robust regularization path algorithm for v-support vector classification. IEEE Trans Neural Netw Learn Syst. doi: 10.1109/TNNLS.2016.2527796 Google Scholar
  34. 34.
    Song XN, Feng ZH, Hu GS, Wu XJ (2017) Half-face dictionary integration for representation-based classification. IEEE Trans Cybern 47(1):142–152Google Scholar
  35. 35.
    Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetGoogle Scholar
  36. 36.
    Shih P, Liu C (2005) Comparative assessment of content-based face image retrieval in different color spaces. Int J Pattern Recog Artif Intell 19(7):873–893Google Scholar
  37. 37.
    Buchsbaum WH (1975) Color TV servicing[M], 3rd edn. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  38. 38.
    Yang J, Liu C, A (2008) Discriminant color space method for face representation and verification on a large-scale database, in: International Conference on Pattern Recognition 2008 (ICPR 2008), Tampa, Florida, USAGoogle Scholar
  39. 39.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720Google Scholar
  40. 40.
    Zhang F (1997) Quaternions and matrices of quaternions. Linear Algebra Appl 251:21–57MathSciNetzbMATHGoogle Scholar
  41. 41.
    Denis P, Carre P, Fernandez-Maloigne C (2007) Spatial and spectral quaternionic approaches for colour images. Comput Vision Image Underst 107:74–87Google Scholar
  42. 42.
    Shi L, Funt B (2007) Quaternion color texture segmentation. Comput Vision Image Underst 107:88–96Google Scholar
  43. 43.
    Xu Y (2012) Quaternion-based discriminant analysis method for color face recognition. PLoS One 7(8):e43493. doi: 10.1371/journal.pone.0043493 Google Scholar
  44. 44.
    Pei SC, Chang JH, Ding JJ (2001) Efficient implementation of quaternion Fourier transform, convolution, and correlation by 2-D complex FFT. IEEE Trans Signal Process 49(11):2783–2797MathSciNetzbMATHGoogle Scholar
  45. 45.
    Pei SC, Chang JH, Ding JJ (2004) Commutative reduced biquaternions and their Fourier transform for signal and image processing applications. IEEE Trans Signal Process 52(7):2012–2031MathSciNetzbMATHGoogle Scholar
  46. 46.
    Nicolas LB, Jerome M (2004) Singular value decomposition of quaternion matrices: a new tool for vector-sensor signal processing. Signal Process 84(7):1177–1199zbMATHGoogle Scholar
  47. 47.
    Yang J, Liu CJ, Yang JY (2010) What kind of color spaces is suitable for color face recognition? Neurocomputing 73:2140–2146Google Scholar
  48. 48.
    Kwak KC, Pedrycz W (2005) Face recognition using a fuzzy Fisherface classifier. Pattern Recognit 38(10):1717–1732Google Scholar
  49. 49.
    Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 15(4):580–585Google Scholar
  50. 50.
    Song XN, Zheng YJ, Wu XJ, Yang XB, Yang JY (2010) A complete fuzzy discriminant analysis approach for face recognition. Appl Soft Comput 10:208–214Google Scholar
  51. 51.
    Yang J, Frangi AF, Yang JY, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244Google Scholar
  52. 52.
    Song X, Yang J, Wu X et al (2011) An optimal symmetrical null space criterion of Fisher discriminant for feature extraction and recognition. Soft Comput 15(2):281–293zbMATHGoogle Scholar
  53. 53.
    Atencia MA, Joya G, Sandoval F (2004) Parametric identification of robotic systems with stable time-varying Hopfield networks. Neural Comput Appl 13:270–280,zbMATHGoogle Scholar
  54. 54.
    Hu ZN, Balakrishnan SN (2005) Parameter estimation in nonlinear systems using Hopfield neural networks. J Aircr 42(1):41–53Google Scholar
  55. 55.
    Alonso H, Mendonça T, Rocha P (2009) Hopfield neural networks for on-line parameter estimation. Neural Netw 22:450–462zbMATHGoogle Scholar
  56. 56.
    Xu Y, Zhu Q, Fan Z, Wang Y, Pan J-S (2013) From the idea of “sparse representation” to a representation-based transformation method for feature extraction. Neurocomputing 113:168–176Google Scholar
  57. 57.
    Xu Y, Zhang D (2010) Represent and fuse bimodal biometric images at the feature level: complex-matrix-based fusion scheme. Opt Eng 49(3):037002Google Scholar
  58. 58.
    Senthilkumar R, Gnanamurthy RK (2014) A detailed survey on 2D and 3D still face and face video databases part I, in: International Conference on Communication and Signal Processing (ICCSP), India, 3-5 April 2014, pp 1405–1409Google Scholar
  59. 59.
    Martinez AM, Benavente R (1998) “The AR face database,” Centre de Visió per Computador (CVC), Universitat Autònoma de Barcelona, Barcelona, Spain, Tech. Rep. 24Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Shuzhe Bao
    • 1
    • 2
  • Xiaoning Song
    • 3
    • 4
    • 5
    Email author
  • Guosheng Hu
    • 6
  • Xibei Yang
    • 7
  • Chunli Wang
    • 1
  1. 1.School of Information Science and TechnologyDalian Maritime University, DLMUDalianChina
  2. 2.School of Computer Science and TechnologyDalian Nationalities University, DLNUDalianChina
  3. 3.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  4. 4.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  5. 5.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina
  6. 6.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  7. 7.Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing University of Science and TechnologyNanjingChina

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