Active Learned Multi-view Face Detection Tree Using Fuzzy Cluster Validity Analysis
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.
KeywordsFace Detection Weak Classifier Cluster Validity Positive Training Negative Training
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