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Face and Gait Recognition Based on Semi-supervised Learning

  • Qiuhong Yu
  • Yilong Yin
  • Gongping Yang
  • Yanbing Ning
  • Yanan Li
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

The performance of the non-contact biometric recognition system is commonly poor when the labeled data set is small. To solve this problem, we perform the semi-supervised learning methods on face and gait to exploit the non-contact unlabeled biometric data. In the paper, the most important work is to apply co-training algorithm to the face and gait recognition system. Besides, we perform experiments on the database built by our group and obtain the results below: Co-training outperforms self-training in improving the performance of the biometric recognition system under same number of templates; Co-training uses fewer template than self-training (one vs. seven) to achieve best performance; Co-training suffers less impact than self-training from the different quality of initial templates.

Keywords

semi-supervised learning gait face self-training co-training 

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References

  1. 1.
    Zhang, D., Zhou, Z.: (2D)2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing, 224–231 (2005)Google Scholar
  2. 2.
    Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT, Cambridge (2006)Google Scholar
  3. 3.
    Roli, F., Didaci, L., Marcialis, G.L.: Adaptive biometric systems that can improve with use. In: Ratha, N., Govindaraju, V. (eds.) Advances in Biometrics: Sensors, Systems and Algorithms, pp. 447–471. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  5. 5.
    Martinez, C., Fuentes, O.: Face recognition using unlabeled data. Journal of Computer Science Research 7(2), 123–129 (2003)Google Scholar
  6. 6.
    Liu, L., Yin, Y., Qin, W., Li, Y.: Gait recognition based on outermost contour. International Journal of Computational Intelligence Systems 4(5), 1090–1099 (2011)Google Scholar
  7. 7.
    Zhou, Z., Li, M.: Semi-supervised learning by disagreement. Knowledge and Information Systems 24(3), 415–439 (2010)CrossRefGoogle Scholar
  8. 8.
    Roli, F., Marcialis, G.L.: Semi-supervised PCA-Based Face Recognition Using Self-training. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 560–568. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Zhu, X.: Semi-supervised learning literature survey. Technical report, Computer Sciences TR 1530, Univ. Wisconsis, Madison, USA (2006)Google Scholar
  10. 10.
    Zhou, Z.-H.: When Semi-supervised Learning Meets Ensemble Learning. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 529–538. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Rattani, A., Marcialis, G.L., Roli, F.: Capturing large intra-class variations of biometric data by template co-updating. In: IEEE Workshop on Biometrics, Int. Conf. on Vision and Pattern Recognition, CVPR 2008, Anchorage, Alaska, USA (2008)Google Scholar
  12. 12.
    Roli, F., Didaci, L., Marcialis, G.L.: Template Co-update in Multimodal Biometric Systems. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 1194–1202. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Gayar, N.E., Shaban, S.A., Hamdy, S.: Face Recognition with semi-supervised learning and Multiple Classifiers. In: Proc. 5th WSEAS Intl. Conf. on Computational Intelligence, Man-Machine System and Cybernetic, vol. 7, pp. 296–301 (2006)Google Scholar
  14. 14.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar
  15. 15.
    Wang, W., Zhou, Z.: A new analysis of co-training. In: Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, pp. 1135–1142 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qiuhong Yu
    • 1
  • Yilong Yin
    • 1
  • Gongping Yang
    • 1
  • Yanbing Ning
    • 1
  • Yanan Li
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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