Face Tracking by Maximizing Classification Score of Face Detector Based on Rectangle Features
Ayumu Hidaka, Kenji Nishida, Takio Kurita · 2006
Face tracking continues to be an important topic in computer vision. We describe a tracking algorithm based on a static face detector. Our face detector is a rectanglefeature- based boosted classifier, which outputs the confidence whether an input image is a face. The function that outputs this confidence, called a score function, contains important information about the location of a moving target. A target that has moved will be located in the gradient direction of a score function from the location before moving. Therefore, our tracker will go to the region where the score is maximum using gradient information of this function. We show that this algorithm works by the combination of jumping to the gradient direction and precise search at the local region.