Fast Gender Recognition by Using a Shared-Integral-Image Approach

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Title:	Fast Gender Recognition by Using a Shared-Integral-Image Approach
Authors:	Shen, Bau-cheng;Chen, Chu-song;Hsu, Hui-huang
Contributors:	淡江大學資訊工程學系
Keywords:	AdaBoost;Gender Recognition;Integral Image;Real AdaBoost;Support Vector Machine
Date:	2009-04
Issue Date:	2012-04-16 09:54:46 (UTC+8)
Publisher:	IEEE Computer Society
Abstract:	We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results.
Relation:	Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), pp.521-524
DOI:	10.1109/ICASSP.2009.4959635
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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