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Conclusion

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

Abstract

Human-centric visual pattern recognition has emerged as one of the most interesting areas of applied research. This is evident from the rising number of publications in this area over the last decade. Perhaps the most prominent area of application is computer vision, where biometric recognition occupies a central part. The need for reliable, accurate, fully automated, and robust biometric recognition systems has motivated intense research in this area. Despite significant progresses being made, the perfect system remains elusive, and research continues on several fronts, the most notable being the construction of highly efficient features for recognition or classification. As Chap.  1 reveals, there are a plethora of features being used for representation of visual patterns.

References

  1. 1.
    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)Google Scholar
  2. 2.
    M.A. Aowal, A.S. Zaman, S.M.M. Rahman, D. Hatzinakos, Static hand gesture recognition using discriminative 2D Zernike moments, in Proceedings of the IEEE TENCON, Bangkok, Thailand (2014) pp. 1–5Google Scholar
  3. 3.
    B.A. Biswas, S.S.I. Khan, S.M.M. Rahman, Discriminative masking for non-cooperative IrisCode recognition, in Proceedings of the International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh (2014), pp. 124–127Google Scholar
  4. 4.
    J. Flusser, T. Suk, B. Zitova, 2D and 3D Image Analysis by Moments (Wiley, UK, 2017)Google Scholar
  5. 5.
    S. Haque, S.M.M. Rahman, D. Hatzinakos, Gaussian-Hermite moment-based depth estimation from single still image for stereo vision. J. Vis. Commun. Image Represent. 41C, 218–295 (2016)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    S. Priyal, P. Bora, A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments. Pattern Recognit. 46(8), 2202–2219 (2013)CrossRefGoogle Scholar
  8. 8.
    S.M.M. Rahman, T. Howlader, D. Hatzinakos, On the selection of 2D Krawtchouk moments for face recognition. Pattern Recognit. 54, 83–93 (2016)Google Scholar
  9. 9.
    S.M.M. Rahman, S.P. Lata, T. Howlader, Bayesian face recognition using 2D Gaussian-Hermite moments. EURASIP J. Image Video Process. 2015, 1–20 (2015)Google Scholar
  10. 10.
    L. Wang, M. Dai, Application of a new type of singular points in fingerprint classification. Pattern Recognit. Lett. 28, 1640–1650 (2007)CrossRefGoogle Scholar
  11. 11.
    M. Wang, W.Y. Chen, X.D. Li, Hand gesture recognition using valley circle feature and Hus moments technique for robot movement control. Measurement 94, 734–744 (2016)CrossRefGoogle Scholar
  12. 12.
    J. Wu, S. Qiu, Y. Kong, Y. Chen, L. Senhadji, H. Shu, MomentsNet: a simple learning-free method for binary image recognition, in Proceedings of the International Conference on Image Processing, Beijing, China (2017), pp. 2667–2671Google 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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