Abstract

We describe a new framework, based on boosting algorithms and cascade structures, to efficiently detect ob jects/faces with occlusions. While our approach is motivated by the work of Viola and Jones, several techniques have been developed for establishing a more general system, including (i) a robust boosting scheme, to select useful weak learners and to avoid overfitting; (ii) reinforcement training, to reduce false-positive rates via a more efiective training procedure for boosted cascades; and (iii) cascading with evidence, to extend the system to handle occlusions, without compromising in detection speed. Experimental results on de- tecting faces under various situations are provided to demonstrate the performances of the proposed method.