Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Design on Face Recognition System Based on pRBFNNs by Obtaining Real Time Image

실시간 이미지 획득을 통한 pRBFNNs 기반 얼굴인식 시스템 설계

  • Received : 2010.09.10
  • Accepted : 2010.12.01
  • Published : 2010.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

Keywords

References

  1. M. J. Er, S. Q. Wu, J. W. Lu, and H. L. Toh, “Face recognition with radical basis function (RBF) neural networks,” IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 697-710, 2002. https://doi.org/10.1109/TNN.2002.1000134
  2. J. Moody and C. J. Darken, “Fast learning in network of locally-tuned processing units,” Neural Comput., vol. 1, pp. 281-294, 1989. https://doi.org/10.1162/neco.1989.1.2.281
  3. Rafael C. Gonzalez, Digital Image Processing, Prentice-Hall, 2002.
  4. P. Viola and M. Jones, “Robust real-time face detection,” Proc. eighth IEEE Int''l Conf. Computer Vision, vol. 20, pp. 1254-1259, July 2001.
  5. A. Haar, “Zur theorie der orthegonalen funktionen system,” Math, Ann, 69, pp. 331-371, 1910. https://doi.org/10.1007/BF01456326
  6. Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” In Computational Learning Theory: Eurocolt '95, Springer-Verlag, pp. 23-37, 1995.
  7. M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991. https://doi.org/10.1162/jocn.1991.3.1.71
  8. J. Park and J. Wsandberg, “Universal approximation using radial basis functions network,” Neural Comput., vol. 3, pp. 246-257, 1991. https://doi.org/10.1162/neco.1991.3.2.246
  9. W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Networks, vol. 9, pp. 601-612, July 1998. https://doi.org/10.1109/72.701174
  10. A. Patrikar and J. Provence, “Pattern classification using polynomial networks,” Electronics Letters, vol. 28, no. 12, pp. 1109-1110, 1992. https://doi.org/10.1049/el:19920700
  11. A. Aiyer, K. Pyun, Y. Z. Huang, D. B. O’Brien, and R. M. Gray, “Lloyd clustering of Gauss mixture models for image compression and classification,” Signal Processing: Image Communication, vol. 20, pp. 459-485, 2005. https://doi.org/10.1016/j.image.2005.03.003
  12. S.-K. Oh, W. Pderycz, and B.-J. Park, “Self-organizing neurofuzzy networks in modeling software data,” Fuzzy Sets and Systems, vol. 145, pp. 165-181, 2004. https://doi.org/10.1016/j.fss.2003.10.009
  13. J. Kennedy and R. Eberhart, “Particle swarmoptimization,” Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995. https://doi.org/10.1109/ICNN.1995.488968
  14. J. Kennedy, “The particle swarm: Social adaptation of knowledge,” Proc. IEEE Int. Conf. Evolutionary Comput., pp. 303-308, 1997.