Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade

Abstract

This paper develops a new approach for extremely fast detection in do-mains where the distribution of positive and negative examples is highly skewed (e.g. face detection or database retrieval). In such domains a cascade of simple classifiers each trained to achieve high detection rates and modest false positive rates can yield a final detector with many desir-able features: including high detection rates, very low false positive rates, and fast performance. Achieving extremely high detection rates, rather than low error, is not a task typically addressed by machine learning al-gorithms. We propose a new variant of AdaBoost as a mechanism for training the simple classifiers used in the cascade. Experimental results in the domain of face detection show the training algorithm yields sig-nificant improvements in performance over conventional AdaBoost. The final face detection system can process 15 frames per second, achieves over 90 % detection, and a false positive rate of 1 in a 1,000,000. 1