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Face Recognition

  • S. M. Mahbubur RahmanEmail author
  • Tamanna Howlader
  • Dimitrios Hatzinakos
Chapter
Part of the Cognitive Intelligence and Robotics book series (CIR)

Abstract

One of the most important applications of visual pattern recognition systems is biometric identification. The need for more secure, reliable, and convenient identification methods has spurred intense research in this field as security becomes one of the most pressing issues of modern times. Apart from security, biometric authentication systems have become indispensable tools in surveillance at airports and sensitive facilities. From everyday tasks such as unlocking a cell phone to more sophisticated applications arising in forensics, banking, border control, and passport verification, the use of biometric authentication is expanding and it is likely to do so well into the future as the technology improves even further.

References

  1. 1.
  2. 2.
  3. 3.
    A.F. Abate, M. Nappi, D. Riccio, G. Sabatino, 2D and 3D face recognition: a survey. Pattern Recognit. Lett. 28(14), 1885–1906 (2007)CrossRefGoogle Scholar
  4. 4.
    Ahonen, T., Hadid, A., M., P.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)Google Scholar
  5. 5.
    S.S. Ali, T. Howlader, S.M.M. Rahman, Pooled shrinkage estimator for quadratic discriminant classifier: an analysis for small sample sizes in face recongition. Int. J. Mach. Learn. Cybern. 9(3), 507–522 (2018)CrossRefGoogle Scholar
  6. 6.
    W. Arnold, V.K. Madasu, W.W. Boles, P.K. Yarlagadda, A feature based face recognition technique using Zernike moments, in Proceedings of RNSA Security Technology Conference (Queensland University of Technology, Melbourne, Australia, 2007), pp. 341–345Google Scholar
  7. 7.
    P.N. Belhumeur, J.P. Hespanha, D.J. Kreigman, Eigenfaces versus Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)Google Scholar
  8. 8.
    M. Bennamoun, Y. Guo, F. Sohel, Feature selection for 2D and 3D face recognition, in Wiley Encyclopedia of Electrical and Electronics Engineering, ed. by J. Webster (Wiley, New York)Google Scholar
  9. 9.
    B.C. Chen, C.-S. Chen, W.H. Hsu, Cross-age reference coding for age-invariant face recognition and retrieval, in Proceedings of the European Conference on Computer Vision (Zurich, Switzerland, 2014), pp. 768–783Google Scholar
  10. 10.
    R.O. Duda, P. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973)Google Scholar
  11. 11.
    N.H. Foon, Y.H. Pang, A.T.B. Jin, D.N.C. Ling, An efficient method for human face recognition using wavelet transform and Zernike moments, in Proceedings of the International Conference on Computer Graphics, Imaging and Visualization (Penang, Malaysia, 2004), pp. 65–69Google Scholar
  12. 12.
    C. Geng, X. Jiang, Face recognition using SIFT features, in Proceedings of the IEEE International Conference on Image Processing (Cairo, Egypt, 2009), pp. 3313–3316Google Scholar
  13. 13.
    G.H. Givens, J.R. Beveridge, Y.M. Lui, D.S. Bolme, B.A. Draper, P.J. Phillips, Biometric face recognition: from classical statistics to future challenges. Wiley Interdiscip. Rev.: Comput. Stat. 5(4), 288–308Google Scholar
  14. 14.
    T. Guha, R. Ward, A sparse reconstruction based algorithm for image and video classification, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (Kyoto, Japan, 2012), pp. 3601–3604Google Scholar
  15. 15.
    J. Haddadnia, K. Faez, M. Ahmadi, An efficient human face recognition system using pseudo-Zernike moment invariant and radial basis function neural networks. Int. J. Pattern Recognit. Artif. Intell. 17(1), 41–62 (2003)Google Scholar
  16. 16.
    X. He, S. Yan, Y. Hu, P. Niyogi, H.J. Zhang, Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)Google Scholar
  17. 17.
    G.B. Huang, V. Jain, E.L. Miller, Unsupervised joint alignment of complex images, in Proceedings of the International Conference on Computer Vision (Janeiro, Brazil, 2007), pp. 1–8Google Scholar
  18. 18.
    G.B. Huang, M. Ramesh, T. Berg, E.L. Miller, Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)Google Scholar
  19. 19.
    R. Jafri, H.R. Arabnia, A survey of face recognition techniques. J. Inf. Process. Syst. 5(2), 41–68 (2009)CrossRefGoogle Scholar
  20. 20.
    A.K. Jain, R.P.W. Duin, J. Mao, Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)Google Scholar
  21. 21.
    G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statisical Learning with Applications in R (Springer, New York, 2013)Google Scholar
  22. 22.
    W.A. Jassim, P. Raveendran, Face recognition using discrete Tchebichef-Krawtchouk transform, in Proceedings of the International Symposium Multimedia (Irvine, CA, USA, 2012), pp. 120–127Google Scholar
  23. 23.
    R.A. Johnson, D.W. Wichern, Applied Multivariate Statistical Analysis, 1st edn. (Prentice-Hall, New Jersey, 1982)Google Scholar
  24. 24.
    J. Kim, J. Choi, J. Yi, M. Turk, Effective representation using ICA for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1977–1981 (2005)Google Scholar
  25. 25.
    M. Kirby, L. Sirovich, Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)Google Scholar
  26. 26.
    J. Kittler, Statistical pattern recognition in image analysis. J. Appl. Stat. 21(1–2), 61–75 (1994)CrossRefGoogle Scholar
  27. 27.
    G. Kukharev, E. Kamenskaya, Application of two-dimensional canonical correlation analysis for face image processing and recognition. Pattern Recognit. Image Anal. 20(2), 210–219 (2010)CrossRefGoogle Scholar
  28. 28.
    O. Ledoit, M. Wolf, Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empir. Financ. 10(5), 1–20 (2003)CrossRefGoogle Scholar
  29. 29.
    O. Ledoit, M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices. J. Multivar. Anal. 88, 365–411 (2004)MathSciNetCrossRefGoogle Scholar
  30. 30.
    S.H. Lee, S. Choi, Two-dimensional canonical correlation analysis. IEEE Signal Process. Lett. 14(10), 1–4 (2007)CrossRefGoogle Scholar
  31. 31.
    G. Lei, J. Zhou, X. LI, X. Gong, Improved canonical correlation analysis and its applications in image recognition. J. Comput. Inf. Syst. 6(11), 3677–3685 (2010)Google Scholar
  32. 32.
    M. Li, B. Yuan, 2D-LDA : a statistical linear discriminant analysis for image matrix. Pattern Recognit. Lett. 26, 527–532 (2005)CrossRefGoogle Scholar
  33. 33.
    S.J. Li, A.K. Jain, Handbook of Face Recognition (Springer, UK, 2011)Google Scholar
  34. 34.
    J. Lu, K. Plataniotis, A. Venetsanopoulos, Regularized discriminant analysis for the small sample size problem in face recognition. Pattern Recognit. Lett. 24, 3079–3087 (2003)CrossRefGoogle Scholar
  35. 35.
    O. Maimon, L. Rokach, Data Mining and Knowledge Discovery Handbook, 2nd edn. (Springer, New York, 2010)Google Scholar
  36. 36.
    S. Majeed, Face recognition using fusion of local binary pattern and Zernike moments, in Proceedings of the IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (Delhi, India, 2016), pp. 1–5Google Scholar
  37. 37.
    Y. Ming, Q. Ruan, R. Ni, Leartning effective features for 3D face recognition, in Proceedings of the IEEE International Conference on Image Processing (Hong Kong, 2010), pp. 2421–2424Google Scholar
  38. 38.
    S. Mitra, N.A. Lazar, Y. Liu, Understanding the role of facial asymmetry in human face identification. Stat. Comput. 17, 57–70 (2007)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Y.H. Pang, A.B.J. Teoh, D.C.L. Ngo, A discriminant pseudo-Zernike moments in face recognition. J. Res. Pract. Inf. Technol. 38(2), 197–211 (2006)Google Scholar
  40. 40.
    P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, W. Worek, Overview of the face recognition grand challenge, in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (San Diego, CA, USA, 2005), pp. 947–954Google Scholar
  41. 41.
    N. Pinto, J.J.D. Carlo, D.D. Cox, How far can you get with a modern face recognition test set using only simple features?, in Proceedings of the Computer Vision and Pattern Recognition (Miami Beach, FL, 2009), pp. 1–8Google Scholar
  42. 42.
    S.J.D. Prince, J.H. Elder, J. Warrell, F.M. Felisberti, Tied factor analysis for face recognition across large pose differences. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1–15 (2008)Google Scholar
  43. 43.
    S.M.M. Rahman, T. Howlader, D. Hatzinakos, On the selection of 2D Krawtchouk moments for face recognition. Pattern Recognit. 54, 83–93 (2016)CrossRefGoogle Scholar
  44. 44.
    S.M.M. Rahman, S.P. Lata, T. Howlader, Bayesian face recognition using 2D Gaussian-Hermite moments. EURASIP J. Image Video Proc. 2015(35), 1–20 (2015)Google Scholar
  45. 45.
    J.S. Rani, D. Devaraj, Face recognition using Krawtchouk moment. Shadhana 37(4), 441–460 (2012)Google Scholar
  46. 46.
    S. Rani, J.D. Devaraj, R. Sukanesh, A novel feature extraction technique for face recognition system, in Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, vol. 2 (Tamil Nadu, India, 2007), pp. 431–435Google Scholar
  47. 47.
    I. Rish, J. Hellerstein, J. Thathachar, An analysis of data characteristics that affect naive Bayes performance. Technical report. RC21993, IBM T.J. Watson Research Center, New York (2001)Google Scholar
  48. 48.
    A. Rivera, J. Castillo, O. Chae, Local directional number pattern for face analysis and expression recognition. IEEE Trans. Image Process. 22, 1740–1752 (2013)MathSciNetCrossRefGoogle Scholar
  49. 49.
    P.E. Shrout, J.L. Fleiss, Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86(2), 420–428 (1979)CrossRefGoogle Scholar
  50. 50.
    C. Singh, N. Mittal, E. Walia, Face recognition using Zernike and complex Zernike moment features. Pattern Recognit. Image Anal. 21(1), 71–81 (2011)CrossRefGoogle Scholar
  51. 51.
    L. Sirovich, M. Kirby, Low-dimensional procedure for characterization of human faces. J. Opt. Soc. Am. 4, 519–524 (1987)CrossRefGoogle Scholar
  52. 52.
    D. Sridhar, I.V.M. Krishna, Face recognition using Tchebichef moments. Int. J. Inf. Netw. Secur. 1(4), 243–254 (2012)Google Scholar
  53. 53.
    T. Kanade J.F. Cohn, Y.L. Tian, Comprehensive database for facial expression analysis, in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (Grenoble, France, 2000), pp. 484–490Google Scholar
  54. 54.
    C.E. Thomaz, D.F. Gillies, R.Q. Feitosa, A new covariance estimate for Bayesian classifiers in biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(2), 214–223 (2004)Google Scholar
  55. 55.
    V.J. Tiagrajah, O. Jamaludin, H.N. Farrukh, Discriminant Tchebichef based moment features for face recognition, in Proceedings of the IEEE International Conference on Signal and Image Processing Applications (Kuala Lumpur, Malaysia, 2011), pp. 192–196Google Scholar
  56. 56.
    Y.L. Tian, T. Kanade, J.F. Cohn, Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001)Google Scholar
  57. 57.
    M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  58. 58.
    P. Viola, M.J. Jones, Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  59. 59.
    L. Wolf, T. Hassner, Y. Taigman, Descriptor based methods in the wild, in Proceedings of the European Conference on Computer Vision (Marseille, France, 2008), pp. 1–14Google Scholar
  60. 60.
    S. Xie, S. Shan, X. Chen, J. Chen, Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans. Image Process. 19(5), 1349–1361 (2010)MathSciNetCrossRefGoogle Scholar
  61. 61.
    S. Xie, S. Shan, X. Chen, X. Meng, W. Gao, Learned local Gabor patterns for face representation and recognition. Signal Process. 89(12), 2333–2344 (2009)CrossRefGoogle Scholar
  62. 62.
    C. Xu, Y. Wang, T. Tan, L. Quan, 3D face recognition based on G-H shape variation, in Lecture Notes in Computer Science: Advances in Biometric Person Authentication, vol. 3338 (2004), pp. 233–244Google Scholar
  63. 63.
    B. Yang, M. Dai, Image analysis by Gaussian-Hermite moments. Signal Process. 91, 2290–2303 (2011)CrossRefGoogle Scholar
  64. 64.
    J. Yang, D. Zhang, A.F. Frangi, J.Y. Yang, Two-dimensional PCA: a new approach of appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • S. M. Mahbubur Rahman
    • 1
    Email author
  • Tamanna Howlader
    • 2
  • Dimitrios Hatzinakos
    • 3
  1. 1.Department of Electrical and Electronic EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
  2. 2.Institute of Statistical Research and TrainingUniversity of DhakaDhakaBangladesh
  3. 3.Department of Electrical and Computer EngineeringUniversity of TorontoTorontoCanada

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