Absolute Contrasts in Face Detection with AdaBoost Cascade

摘要

Object detection using AdaBoost cascade classifier was introduced by Viola and Jones in December 2001. This paper presents a modification of their method which allows to obtain even 4-fold decrease in false rejection rate, keeping false acceptance rate - as well as the classifier size and training time - at the same level. Such an improvement is achieved by extending original family of weak classifiers, which is searched through in every step of AdaBoost algorithm, with classifiers calculating absolute value of contrast. Test results given in the paper come from a face localization problem, but the idea of absolute contrasts can be applied to detection of other types of objects, as well.