On Asymmetric Classifier Training for Detector Cascades
This paper examines the Asymmetric AdaBoost algorithm introduced by Viola and Jones for cascaded face detection. The Viola and Jones face detector uses cascaded classifiers to successively filter, or reject, non-faces. In this approach most non-faces are easily rejected by the earlier classifiers in the cascade, thus reducing the overall number of computations. This requires earlier cascade classifiers to very seldomly reject true instances of faces. To reflect this training goal, Viola and Jones introduce a weighting parameter for AdaBoost iterations and show it enforces a desirable bound. During their implementation, a modification to the proposed weighting was introduced, while enforcing the same bound. The goal of this paper is to examine their asymmetric weighting by putting AdaBoost in the form of Additive Regression as was done by Friedman, Hastie, and Tibshirani. The author believes this helps to explain the approach and adds another connection between AdaBoost and Additive Regression.
KeywordsTraining Sample Loss Function Additive Regression False Acceptance Rate Bias Weighting
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