Skip to main content

Abstract

We live in a world that is built upon patterns. What is a pattern? The Oxford dictionary defines a pattern as a repeated decorative design. In the language of pattern recognition, however, a pattern has been described as an entity that could be given a name [36]. Thus, the bird, boat, buildings, and people that we see in Fig. 1.1 are all examples of patterns. Recognizing patterns in the environment is one of the fundamental signs of intelligent behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Human Visual Pathway. https://commons.wikimedia.org/wiki/File:Human_visual_pathway.svg

  2. A. Abbas, M. Khalil, S. Abdel Hay, H.M.A. Fahmy, Illumination invariant face recognition in logarithm discrete cosine transform domain, in Proceedings of the IEEE International Conference on Image Processing, Cairo, Egypt (2009), pp. 4157–4160

    Google Scholar 

  3. T. Ahonen, A. Hadid, P. M., Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Google Scholar 

  4. A. Albiol, D. Monzo, A. Martin, J. Sastre, A. Albiol, Face recognition using HOGEBGM. Pattern Recognit. Lett. 29(10), 1537–1543 (2011)

    Article  Google Scholar 

  5. F. Alonso-Fernandez, P. Tome-Gonzalez, V. Ruiz-Albacete, J. Ortega-Garcia, Iris recognition based on SIFT features, in Proceedings of the International Conference on Biometrics, Identity and Security, Tampa, FL, USA (2009), pp. 1–8

    Google Scholar 

  6. A. Aravindan, S.M. Anzar, Robust partial fingerprint recognition using wavelet SIFT descriptors. Pattern Anal. Appl. 20(4), 963–979 (2017)

    Article  MathSciNet  Google Scholar 

  7. H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  8. C. Belcher, Y. Du, Region-based SIFT approach to iris recognition. Opt. Lasers Eng. 47(1), 139–147 (2009)

    Article  Google Scholar 

  9. P.N. Belhumeur, J.P. Hespanha, D.J. Kreigman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Google Scholar 

  10. S. Berretti, B.B. Amor, M. Daoudi, A. del Bimbo, 3D facial expression recognition using SIFT descriptors of automatically detected keypoints. Vis. Comput. 27, 1021–1036 (2011)

    Article  Google Scholar 

  11. A. Caliskan, O.F. Ertugrul, Wavelet transform based fingerprint recognition, in Proceedings of the Signal Processing and Communications Applications Conference, Malatya, Turkey (2015) pp. 786–793

    Google Scholar 

  12. L. Cament, F. Galdames, K. Bowyer, C.A. Perez, Face recognition under pose variation with active shape model to adjust gabor filter kernels and to correct feature extraction location, in Proceedings of the IEEE International Conference Workshops on Automatic Face and Gesture Recognition, Ljubljana, Slovenia (2015), pp. 1–6

    Google Scholar 

  13. P. Carcagni, M.D. Coco, M. Leo, C. Distante, Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus 4(645), 1–25 (2015)

    Google Scholar 

  14. R. Carro, J. Larios, E. Huerta, R. Caporal, F. Cruz, Face recognition using SURF, in Lecture Notes in Computer Science: Intelligent Computing Theories and Methodologies, vol. 9225 (2015), pp. 316–326

    Google Scholar 

  15. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA (2005), pp. 1–8

    Google Scholar 

  16. N. Dalal, B. Triggs, Half iris biometric system based on HOG and LIOP, in Proceedings of the International Conference on Frontiers of Signal Processing, Warsaw, Poland (2016), pp. 99–103

    Google Scholar 

  17. M. Dale, M.A. Joshi, M. Sahu, DCT feature based fingerprint recognition, in Proceedings of the International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia (2007), pp. 611–615

    Google Scholar 

  18. J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1148–1161 (1993)

    Google Scholar 

  19. J. Daugman, How iris recognition works? IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)

    Article  Google Scholar 

  20. C.L. Deepika, A. Kandaswamy, P. Gupta, Orthogonal moments for efficient feature extraction from line structure based biometric images. Lecture Notes in Computer Science: Intelligent Computing Theories and Applications, vol. 7390 (2012), pp. 656–663

    Google Scholar 

  21. O. Deniz, G. Bueno, J. Salido, F.D. la Torre, Face recognition using histograms of oriented gradients. Pattern Recognit. Lett. 32(12), 1598–1603 (2011)

    Article  Google Scholar 

  22. J.J. DiCarlo, D. Zoccolan, N. Rust, How does the brain solve visual object recognition? Neuron 73(3), 415–434 (2012)

    Article  Google Scholar 

  23. S. Farokhi, S.M. Shamsuddin, U. Sheikh, J. Flusser, M. Khansari, K. Jafari-Khouzani, Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform. Digit. Signal Process. 31, 13–27 (2014)

    Article  Google Scholar 

  24. J. Flusser, T. Suk, B. Zitova, 2D and 3D Image Analysis by Moments (Wiley, New York, 2017)

    MATH  Google Scholar 

  25. J. Flusser, B. Zitova, T. Suk, Moments and Moment Invariants in Pattern Recognition (Wiley, New York, 2009)

    Book  Google Scholar 

  26. C. Geng, X. Jiang, Face recognition using SIFT features, in Proceedings of the IEEE International Conference on Image Processing, Cairo, Egypt (2009), pp. 3313–3316

    Google Scholar 

  27. S.O. Gonzaga, A method for fingerprint image identification based on Gabor filter and power spectrum. Pattern Recognit. Image Anal. 20(2), 201–209 (2010)

    Article  Google Scholar 

  28. Q. Haq, M. Javed, Q. Haq, Efficient and robust approach of iris recognition through Fisher linear discriminant analysis method and principal component analysis method, in Proceedings of the IEEE International Multitopic Conference, Karachi, Pakistan (2008), pp. 218–225

    Google Scholar 

  29. M. Hassaballah, A. Abdelmgeid, H. Alshazly, Image features detection, description and matching, Studies in Computational Intelligence, vol. 630 (Springer, Berlin, 2016), pp. 11–45

    Google Scholar 

  30. S. He, C. Zhang, P. Hao, Clustering-based descriptors for fingerprint indexing and fast retrieval, in Lecture Notes in Computer Science: Asian Conference on Computer Vision 2009, vol. 5994 (2010), pp. 354–363

    Google Scholar 

  31. C. Hermite, Sur un Nouveau Developpement en Serie des Fonctions. Gauthier-Villars (in French) (1864)

    Google Scholar 

  32. M.K. Hu, Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)

    Article  Google Scholar 

  33. P. Huang, C. Chiang, J. Liang, Iris recognition using Fourier-wavelet features, in Lecture Notes in Computer Science: Audio- and Video-Based Biometric Person Authentication, vol. 3546 (2005), pp. 14–22

    Google Scholar 

  34. S.M. Imran, S.M.M. Rahman, D. Hatzinakos, Differential components of discriminative 2D Gaussian-Hermite moments for recognition of facial expressions. Pattern Recognit. 56, 100–115 (2016)

    Article  Google Scholar 

  35. A. Jain, S. Prabhakar, L. Hong, S. Pankanti, Filterbank-based fingerprint matching. IEEE Trans. Image Process. 9(5), 846–859 (2000)

    Article  Google Scholar 

  36. 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 

  37. R.A. Johnson, D.W. Wichern, Applied Multivariate Statistical Analysis, 1st edn. (Prentice-Hall, Upper Saddle River, 1982)

    MATH  Google Scholar 

  38. J.K. Kamarainen, Gabor features in image analysis, in Proceedings of the IEEE International Conference on Image Processing Theory, Tools and Applications, Istanbul, Turkey (2012), pp. 1–2

    Google Scholar 

  39. S. Kittusamy, V. Chakrapani, Facial expressions recognition using eigenfaces. J. Comput. Sci. 8(10), 1674–1679 (2012)

    Article  Google Scholar 

  40. J. Krizaj, V. Struc, N. Pavesic, Adaptation of SIFT features for face recognition under varying illumination, in Proceedings of the International Convention MIPRO, Opatija, Croatia (2016), pp. 691–694

    Google Scholar 

  41. S.M. Lajevardi, Z.M. Hussain, Higher order orthogonal moments for invariant facial expression recognition. Digit. Signal Process. 20(6), 1771–1779 (2010)

    Article  Google Scholar 

  42. C. Li, W. Zhou, S. Yuan, Iris recognition based on a novel variation of local binary pattern. Vis. Comput. 31(10), 1419–1429 (2015)

    Article  Google Scholar 

  43. H. Li, J. Ellis, L. Zhang, S.F. Chang, PatternNet: visual pattern mining with deep neural network, in Proceedings of the ACM International Conference on Multimedia Retrieval, Yokohama, Japan (2018), pp. 291–299

    Google Scholar 

  44. W.S. Lin, Y.L. Wu, W.C. Hung, C.Y. Tang, A study of real-time hand gesture recognition using SIFT on binary images, Smart Innovation, Systems and Technologies: Advances in Intelligent Systems and Applications, vol. 21 (2013), pp. 235–246

    Google Scholar 

  45. C. Liu, D. Dai, Face recognition using dual tree complex wavelet features. IEEE Trans. Image Process. 18(11), 2593–2599 (2009)

    Article  MathSciNet  Google Scholar 

  46. D. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  47. L. Ma, T. Tan, Y. Wang, D. Zhang, Local intensity variation analysis for iris recognition. Pattern Recognit. 37, 1287–1298 (2004)

    Article  Google Scholar 

  48. A. Maqueda, C. del Blanco, F. Jaureguizar, N. Garcia, Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns. Comput. Vis. Image Underst. 141, 126–137 (2015)

    Article  Google Scholar 

  49. H. Mehrotra, P. Sa, B. Majhi, Fast segmentation and adaptive SURF descriptor for iris recognition. Math. Comput. Model. 58(1–2), 132–146 (2013)

    Article  Google Scholar 

  50. A. Misra, A. Takashi, T. Okatani, K. Deguchi, Hand gesture recognition using histogram of oriented gradients and partial least squares regression, in Proceedings of the IAPR Conference on Machine Vision Applications, Nara, Japan (2011), pp. 479–482

    Google Scholar 

  51. R. Mukundan, S. Ong, P. Lee, Image analysis by Tchebichef moments. IEEE Trans. Image Process. 10(9), 1357–1364 (2001)

    Article  MathSciNet  Google Scholar 

  52. R. Mukundan, K. Ramakrishnan, Moment Functions in Image Analysis: Theory and Applications (World Scientific, Singapore, 1998)

    Book  Google Scholar 

  53. A. Nabatchian, E. Abdel-Raheem, M. Ahmadi, Human face recognition using different moment invariants: a comparative study, in Proceedings of the Congress on Image and Signal Processing, Sanya, Hainan, China (IEEE, 2008), pp. 661–666

    Google Scholar 

  54. L. Nanni, A. Lumini, Local binary patterns for a hybrid fingerprint matcher. Pattern Recognit. 41, 3461–3466 (2008)

    Article  Google Scholar 

  55. L. Nanni, A. Lumini, S. Brahnam, Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39, 3634–3641 (2012)

    Article  Google Scholar 

  56. T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Google Scholar 

  57. G. Papakostas, Over fifty years of image moments and moment invariants, in Moments and Moment Invariants: Theory and Applications (Science Gate Publishing, 2014), pp. 3–32

    Google Scholar 

  58. C. Park, H. Park, Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis. Pattern Recognit. 38(4), 495–503 (2005)

    Article  Google Scholar 

  59. M. Pawlak, Image Analysis by Moments: Reconstruction and Computational Aspects (Oficyna Wydawn. Politechn., Wrocklaw, 2006)

    MATH  Google Scholar 

  60. P. Premaratne, Human Computer Interaction Using Hand Gestures (Springer, Singapore, 2014)

    Book  Google Scholar 

  61. H. Qader, A. Ramli, S. Al-Haddad, Fingerprint recognition using Zernike moments. Int. Arab J. Inf. Technol. 4(4), 372–376 (2007)

    Google Scholar 

  62. S.M.M. Rahman, S.P. Lata, T. Howlader, Bayesian face recognition using 2D Gaussian-Hermite moments. EURASIP J. Image Video Process. 2015(35), 1–20 (2015)

    Google Scholar 

  63. S.M.M. Rahman, M.M. Reza, Q. Hassani, Low-complexity iris recognition method using 2D Gauss-Hermite moments, in Proceedings of the International Symposium Image and Signal Processing and Analysis, Trieste, Italy (2013), pp. 142–146

    Google Scholar 

  64. E. Salahat, M. Qasaimeh, Recent advances in features extraction and description algorithms: a comprehensive survey, in Proceedings of the International Conference Industrial Technology, Toronto, ON, Canada (2017), pp. 1059–1063

    Google Scholar 

  65. A.K. Sao, B. Yegnanarayana, On the use of phase of the Fourier transform for face recognition under variations in illumination. Signal Image Video Process. 4(3), 353–358 (2010)

    Article  Google Scholar 

  66. E.S. Serra, Understanding human-centric images: from geometry to fashion. Ph.D. thesis, BarcelonaTech: Automatica, Robotica I Visio, Universitat Politecnica de Catalunya (2015)

    Google Scholar 

  67. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio, Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3)

    Google Scholar 

  68. C. Shan, S. Gong, P. McOwan, Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)

    Article  Google Scholar 

  69. Y. Sheng, L. Shen, Orthogonal Fourier-Mellin moments for invariant pattern recognition. J. Opt. Soc. Am. 11(6), 1748–1757 (1994)

    Article  Google Scholar 

  70. M. Siddiqi, R. Ali, A. Khan, Y. Park, S. Lee, Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE Trans. Image Process. 24(4), 1386–1398 (2015)

    Article  MathSciNet  Google Scholar 

  71. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in Proceedings of the International Conference on Learning Representations

    Google Scholar 

  72. H. Soyel, H. Demirel, Facial expression recognition based on discriminative scale invariant feature transform. Electron. Lett. 46(5), 343–345 (2010)

    Article  Google Scholar 

  73. P. Sykora, P. Kamencay, R. Hudec, Comparison of SIFT and SURF methods for use on hand gesture recognition based on depth map. AASRI Procedia 9, 19–24 (2014)

    Article  Google Scholar 

  74. C.W. Tan, A. Kumar, Accurate iris recognition at a distance using stabilized iris encoding and Zernike moments phase features. IEEE Trans. Image Process. 23(9), 3962–3974 (2014)

    Article  MathSciNet  Google Scholar 

  75. M.R. Teague, Image analysis via a general theory of moments. J. Opt. Soc. Am. 70(8), 920–930 (1980)

    Article  MathSciNet  Google Scholar 

  76. M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  77. L. Wang, M. Dai, An effective method for extracting singular points in fingerprint images. Int. J. Electron. Commun. 60(9), 671–676 (2006)

    Article  Google Scholar 

  78. Z. Wang, Q. Ruan, G. An, Facial expression recognition using sparse local fisher discriminant analysis. Neurocomputing 174(B), 756–766 (2016)

    Google Scholar 

  79. P.T. Yap, R. Paramesran, S.H. Ong, Image analysis by Krawtchouk moments. IEEE Trans. Image Process. 12(11), 1367–1377 (2003)

    Article  MathSciNet  Google Scholar 

  80. R. Zhou, D. Zhong, J. Han, Fingerprint identification using SIFT-based minutia descriptors and improved all descriptor-pair matching. Sensors 13(3), 3142–3156 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. M. Mahbubur Rahman .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rahman, S.M.M., Howlader, T., Hatzinakos, D. (2019). Introduction. In: Orthogonal Image Moments for Human-Centric Visual Pattern Recognition. Cognitive Intelligence and Robotics. Springer, Singapore. https://doi.org/10.1007/978-981-32-9945-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9945-0_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9944-3

  • Online ISBN: 978-981-32-9945-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics