A binary decision tree implementation of a boosted strong classifier

Authors:
S. Kevin Zhou
Affiliations:
Integrated Data Systems Department, Siemens Corporate Research, Princeton, NJ
Venue:
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Year:
2005

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Abstract

Viola and Jones [1] proposed the influential rapid object detection algorithm. They used AdaBoost to select from a large pool a set of simple features and constructed a strong classifier of the form {∑jαjhj(x) ≥ θ} where each hj(x) is a binary weak classifier based on a simple feature. In this paper, we construct, using statistical detection theory, a binary decision tree from the strong classifier of the above form. Each node of the decision tree is just a weak classifier and the knowledge of the coefficients αjis no longer needed. Also, the binary tree has a lot of early exits. As a result, we achieve an automatic speedup that always makes the rapid Viola and Jones algorithm rapider.