NDLI: Gender classification based on multi-classifiers fusion for Human-Robot interaction

Content Provider	IEEE Xplore Digital Library
Author	Luo, R.C. Tzu-Ta Lin Ming-Chieh Tsai
Copyright Year	2011
Description	Author affiliation: Intelligent Robotics and Automation Laboratory, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617 Taiwan, China (Luo, R.C.; Tzu-Ta Lin; Ming-Chieh Tsai)
Abstract	In the robotic area, robot will take some actions depending on gender or person. For example, according to gender, robot can change different listening-modes for voice recognition to improve recognition accuracy. Besides, we can recognize face based on gender to change predicting model from male or female database. Therefore, we develop a Human-Robot interaction through gender recognition. In this paper, we adopt multiple classifiers based on support vector machine to recognize gender in low-resolution facial images (36-by-36 pixels); because fusing multiple classifiers usually promises higher classification accuracy than using individual classifier. Therefore, we conduct our research on comparing bootstrap aggregating (Bagging) and Adaboost. In conclusion, we find Adaboost with image pixels as input indeed facilitates the gender classification.
Starting Page	796
Ending Page	800
File Size	1072733
Page Count	5
File Format	PDF
ISBN	9781424493104
e-ISBN	9781424493128
e-ISBN	9781424493111
DOI	10.1109/ISIE.2011.5984260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-27
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Training Bagging Robots Support vector machines Classification algorithms Accuracy
Content Type	Text
Resource Type	Article

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