한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제21권4호
  • /
  • Pages.471-477
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  • 2018
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  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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Human Face Recognition used Improved Back-Propagation (BP) Neural Network

  • Zhang, Ru-Yang (Dept. of Information & Communication Eng., Graduate School, Tongmyong University) ;
  • Lee, Eung-Joo (Dept. of Information & Communication Eng., Tongmyong University)
  • 투고 : 2018.03.12
  • 심사 : 2018.04.06
  • 발행 : 2018.04.30
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초록

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

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참고문헌

  1. H.H. Nam, B.J. Kang, and K.H. Park, "Comparison of Computer and Human Face Recognition According to Facial Components," Journal of Korea Multimedia Society, Vol. 37, No. 21, pp. 40-50, 2012.
  2. H.J. Moon, and S.H. Kim, "Comparison of Computer and Human Face Recognition According to Facial Components," Journal of Korea Multimedia Society, Vol. 6, No. 2, pp. 247-258, 2013.
  3. W.O. Lee, Y.H. Park, E.C. Lee, H.K. Lee, and K.R. Park, "Tracking and Face Recognition of Multiple People Based on GMM, LKT and PCA," Journal of Korea Multimedia Society, Vol. 15, No. 4, pp. 449-471, 2012. https://doi.org/10.9717/kmms.2012.15.4.449
  4. Z. Li, and X. Fu, "Research on Performance of Three Improved BP Algorithms Based on PCA," Journal of Computer Engineering, Vol. 37, No. 21, pp. 108-110, 2011.
  5. R. Hecht-Nielsen, "Theory of the Back Propagation Neural Network," Journal of the International Joint Conference on Neural Networks, Vol. 1, No. 1, pp. 455, 1988.
  6. M. Kirby, and L. Sirovich, "Application of the Karhunen-loeve Procedure for the Characterization of Human Faces," Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, pp. 103-108, 2002.
  7. M. Riedmiller, and H. Braun, "A Direct Adaptive Method for Faster Back Propagation Learning: The RPROP Algorithm," Proceedings of the IEEE International Conference on Neural Networks, Vol 1, pp. 586-591, 1993.
  8. Z.D. Long, G. Wen, and D.B. Zhao, et al., "Faces Recognition Based on Singular Value Decomposition and Discriminant KL Projection," Journal of Software, Vol. 14, No. 4, pp. 783-788, 2003.
  9. Y.S. Xu, J.H. Gu, Z. Tao, D. Wu, and M.C. Zhu, "Handwritten Character Recognition Based on Improved BP Neuralnetwork," Journal of Communication Technology, Vol. 5, No. 44, pp. 106-109, 2011.
  10. C.J. Zhao, "Research on Expression Recognition Based on Optimized BP Neural Network," Proceedings of International Conference on Industrial Engineering and Engineering Management, pp. 1803-1806, 2009.
  11. L. Yan, "Fusion Method of PCA and BP Neural Network for Face Recognition," Proceedings of the International Conference on Computer Science and Service System, pp. 3256-3259, 2011.
  12. B. Messaoud, M. Lamia, H. Farid, and M. Cheriet, et al., "A Guessoum. Face Recognition Based on 2DPCA, DIAPCA and DIA2 DPCA in DCT Domain," Proceedings of the International Multi-conference on Systems, Signals and Devices pp. 1-6, 2008.