Need explanation for viola-jones boosting

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Actually I would like to mention that your conclusions are completely wrong. First of all, a weak classifier corresponds to a stage in code, which is not a single feature! It starts with 1 feature and adds features untill demands are reached. Each weak classifier is thus a combo of multiple features.

Thank you @StevenPuttemans! I just go by seminal 2001 paper on boosting by Viola&Jones: "The weak learner is constrained so that each weak classifier returned can depend on only a single feature. As a result each stage of the boosting process, which selects a new weak classifier, can be viewed as a feature selection process."[1] But English is not my first language.

[1] "Rapid Object Detection using a Boosted Cascade of Simple Features", Viola & Jones, CVPR 2001.

Hmm I would have to look deeper into this. I always considered openCV to use combinations of features for each stage - aka a weak classifier - because of the complexity that raises and according to the fact that you can choose to use stumps or tree based criteria. I will get back to you on this!