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.
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Yu, Q., Yin, Y., Yang, G., Ning, Y., Li, Y. (2012). Face and Gait Recognition Based on Semi-supervised Learning. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_36
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DOI: https://doi.org/10.1007/978-3-642-33506-8_36
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