Cascade AdaBoost classifiers with stage optimization for face detection

In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications, such as tele-conferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, the weights of weak classifiers may not be optimized. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which yields better generalization performance.