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Redundant Parallel Hopfield Network Configurations: A New Approach to the Two-Dimensional Face Recognitions

병렬 다중 홉 필드 네트워크 구성으로 인한 2-차원적 얼굴인식 기법에 대한 새로운 제안

  • 김영택 (경성대학교 컴퓨터공학과) ;
  • Received : 2017.08.11
  • Accepted : 2017.11.07
  • Published : 2018.02.28

Abstract

Interests in face recognition area have been increasing due to diverse emerging applications. Face recognition algorithm from a two-dimensional source could be challenging in dealing with some circumstances such as face orientation, illuminance degree, face details such as with/without glasses and various expressions, like, smiling or crying. Hopfield Network capabilities have been used specially within the areas of recalling patterns, generalizations, familiarity recognitions and error corrections. Based on those abilities, a specific experimentation is conducted in this paper to apply the Redundant Parallel Hopfield Network on a face recognition problem. This new design has been experimentally confirmed and tested to be robust in any kind of practical situations.

얼굴인식 분야의 관심은 다양한 신흥분야의 응용에 의해 증강되고 있다. 2-차원적인 인식 알고리즘의 필요성이 어떤 변화무쌍한 환경들, 예를 들어서, 얼굴의 방향이나 조명도, 안경의 유무, 혹은 웃음과 울음 같은 다양한 표정변화의 처리에 적합할 수 있게 고찰 되어 지고 있다. 형상 기억이나 일반화 과정, 유사성 인식, 오류수정 등에 장점을 가지고 있는 홉 필드 네트워크의 기능을 바탕으로 하여 본 연구에서는 새로운 방법의 병렬적인 다중 홉 필드 네트워크를 구성하여 변화에 강한 얼굴표정 인식의 실험을 2-차원 알고리즘으로 실시하였고 결과가 실제적인 얼굴 형상 환경 변화에서 강한 적응성을 가지고 있음을 확인하였다.

Keywords

References

  1. T. Heseltine, N. Pears, and J. Austin, "Three-Dimensional Face Recognition Using Surface Space Combinations," BMVC, Vol.4. 2004.
  2. A. M. Bronstein, M. M. Bronstein, and R. Kimmel, "Three-dimensional face recognition," International Journal of Computer Vision, Vol.64, No.1, pp.5-30, 2005. https://doi.org/10.1007/s11263-005-1085-y
  3. S. Gupta, M. K. Markey, and A. C. Bovik, "Anthropometric 3D face recognition," International Journal of Computer Vision, Vol.90, No.3, pp.331-349, 2010. https://doi.org/10.1007/s11263-010-0360-8
  4. C. C. Queirolo, L. Silva, O. R. Bellon, and M. P. Segundo, "3D face recognition using simulated annealing and the surface interpenetration measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.2, pp.206-219, 2010. https://doi.org/10.1109/TPAMI.2009.14
  5. L. Spreeuwers, "Fast and accurate 3d face recognition," International Journal of Computer Vision, Vol.93, No.3, pp. 389-414, 2011. https://doi.org/10.1007/s11263-011-0426-2
  6. J. Hopfield, "Neural Networks and Physical Systems with Emergent Collective Computational Abilities," Proceedings of the National Academy of Sciences, Vol.79, pp.2554-2558, 1982. https://doi.org/10.1073/pnas.79.8.2554
  7. S. Haykin, "Neural Networks: A Comprehensive Foundation," 2nd ed. Prentice Hall, 1999.
  8. D. J. C. Mackay, "Information Theory, Inference and Learning Algorithms," Cambridge University Press, 2003.
  9. J. Amit and A. Treves, "Associative Memory Neural Network with Low Temporal Spiking Rates," Proceedings of the National Academy of Sciences, Vol.86, pp.7871-7875, 1989. https://doi.org/10.1073/pnas.86.20.7871
  10. AT&T Laboratories Cambridge, The Database of Faces [internet], http://www.cl.cam.ac.uk/research/dtg/attarctive/ facedatabase.html.
  11. K. Zuiderveld, "Contrast limited adaptive histogram equalization," Academic Press Professional, Inc. Graphics gems IV, pp.474-485, 1994.
  12. K. Gurney, "An Introduction to Neural Networks," Taylor & Francis, 1997.
  13. M. M. Kasar, D. Bhattacharyya, and T. H. Kim, "Face recognition using neural network: a review," International Journal of Security and Its Applications, Vol.10, No.3, pp.81-100, 2016.