Inter-stage feature propagation in cascade building with AdaBoost

A modification of the cascaded detector with the Ada-Boost trained stage classifiers is proposed and brought to bear on the face detection problem. The cascaded detector is a sequential classifier with the ability of early rejection of easy samples. Each decision in the sequence is made by a separately trained classifier, a stage classifier. In proposed modification the features from one stage of training are propagated to the next stage classifier. The proposed intra-stage feature propagation is shown to be greedily optimal, does not increase computational complexity of the stage classifier and leads to shorter stage classifiers and accordingly to faster detectors. A cascaded face detector is built with the intra-stage feature propagation and is compared with the Viola and Jones approach. The same detection and false positive rates are achieved with a detector that is 25% faster and consists of only two thirds of the weak classifiers needed for a cascade trained by the Viola and Jones approach. The latter property facilitates hardware implementation, the former opens scope for the increase in the search space, e.g., the range of scales at which faces are sought.