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FloatBoost Learning and Statistical Face Detection
September 2004 (vol. 26 no. 9)
pp. 1112-1123
ASCII Text
x 
 
Stan Z. Li, ZhenQiu Zhang, "FloatBoost Learning and Statistical Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1112-1123, September, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2004.68,
author = {Stan Z. Li and ZhenQiu Zhang},
title = {FloatBoost Learning and Statistical Face Detection},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {26},
number = {9},
issn = {0162-8828},
year = {2004},
pages = {1112-1123},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.68},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - FloatBoost Learning and Statistical Face Detection
IS - 9
SN - 0162-8828
SP1112
EP1123
EPD - 1112-1123
A1 - Stan Z. Li,
A1 - ZhenQiu Zhang,
PY - 2004
KW - Pattern classification
KW - boosting learning
KW - AdaBoost
KW - FloatBoost
KW - feature selection
KW - statistical models
KW - face detection.
VL - 26
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Pattern classification, boosting learning, AdaBoost, FloatBoost, feature selection, statistical models, face detection.
Citation:
Stan Z. Li, ZhenQiu Zhang, "FloatBoost Learning and Statistical Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1112-1123, Sept. 2004, doi:10.1109/TPAMI.2004.68
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