Inter-Stage Feature Propagation in Cascade Building with AdaBoost

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Inter-Stage Feature Propagation in Cascade Building with AdaBoost
A modification of the cascaded detector with the AdaBoost 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 Jone...
Jan Sochman, Jiri Matas
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Jan Sochman, Jiri Matas
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