FloatBoost learning and statistical face detection
- 26 July 2004
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 26 (9), 1112-1123
- https://doi.org/10.1109/tpami.2004.68
Abstract
A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.This publication has 33 references indexed in Scilit:
- Boosting the margin: a new explanation for the effectiveness of voting methodsThe Annals of Statistics, 1998
- Arcing classifier (with discussion and a rejoinder by the author)The Annals of Statistics, 1998
- Neural network-based face detectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- Probabilistic visual learning for object representationIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Face recognition by elastic bunch graph matchingIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Joint induction of shape features and tree classifiersIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Floating search methods in feature selectionPattern Recognition Letters, 1994
- A theory of the learnableCommunications of the ACM, 1984
- Summed-area tables for texture mappingACM SIGGRAPH Computer Graphics, 1984