BibTeX
@MISC{Wu_real-timegender,
author = {Bo Wu and Haizhou Ai and Chang Huang},
title = {Real-time Gender Classification},
year = {}
}
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Abstract
This paper introduces an automatic real-time gender classification system. The system consists of mainly three modules, face detection, normalization and gender classification. The LUT-type weak classifier based Adaboost learning method is proposed for training both face detector and gender classifier, and a Simple Direct Appearance Model (SDAM) based method is developed to detect the facial landmark points for face normalization. This results in an integrated system with rather good performance. Experiment results on both pictures from World Wide Web and real-time video clips are reported to demonstrate its effectiveness and robustness.
