Abstract
An active learned face detection tree based on FloatBoost method is proposed to accommodate the in-class variability of multi-view faces. To handle the computation resource constraints to the size of training example set, an embedded Bootstrap example selection algorithm is proposed, which leads to a more effective predictor. The tree splitting procedure is realized through dividing face training examples into the optimal sub-clusters using the fuzzy c-means algorithm together with a new cluster validity function based on the modified partition fuzzy degree. Then each sub-cluster of face examples is conquered with the FloatBoost learning to construct branches in the node of the detection tree. During training, the proposed algorithm is much faster than the original detection tree. The experimental results illustrate that the proposed detection tree is more efficient than the original one while keeping its detection speed. And the E-Bootstrap strategy outperforms the Bootstrap one in selecting relevant examples.
This work was partially supported by the National Natural Science Foundation of China (No.60202004) the Key Project of Chinese Ministry of Education (No.104173), and the Program for New Century Excellent Talents in University (NCET-04-0948), China.
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Tian, C., Gao, X., Li, J. (2006). Active Learned Multi-view Face Detection Tree Using Fuzzy Cluster Validity Analysis. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_100
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DOI: https://doi.org/10.1007/11881599_100
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