GibbsBoost顔検出と映像監視業務への応用

概要

論文の詳細を見る
We propose non-deterministic methods for automatically detecting occlusions of people's faces by superimposed symbols. We trained a face detector using an ensemble-learning algorithm (GibbsBoost) that is based on the sequential Monte Carlo method. We implemented an occlusion detector using a mixture of two discriminant functions that were related to the size of the detected face region and the occluded face area. One realization of this detector achieved a true positive detection rate of 90%. We present experimental results and discuss possibilities for further improvements.
社団法人映像情報メディア学会の論文
2008-03-01