A Valid Multi-View Face Detection Tree Based on Floatboost Learning

A novel face detection tree based on floatboost learning is proposed to accommodate the in-class variability of multi-view faces. The tree splitting procedure is realized through dividing face training examples into the optimal sub-clusters using the fuzzy c-means (FCM) 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 on the CMU and our home-brew test database illustrate that the proposed detection tree is more efficient than the original one while keeping its detection speed.