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)


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.


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


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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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