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		<doi>10.1109/SIBGRAPI.2012.41</doi>
		<citationkey>El-DinMousMahd:2012:MiTwGe</citationkey>
		<title>A mixture of two gender classification experts</title>
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		<year>2012</year>
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		<size>304 KiB</size>
		<author>El-Din, Yomna Safaa,</author>
		<author>Moustafa, Mohamed N.,</author>
		<author>Mahdi, Hani,</author>
		<affiliation>Department of Computer and Systems Engineering, Ain Shams University, Cairo, Egypt </affiliation>
		<affiliation>Department of Computer Science and Engineering, American University in Cairo, New Cairo, Egypt </affiliation>
		<affiliation>Department of Computer and Systems Engineering, Ain Shams University, Cairo, Egypt</affiliation>
		<editor>Freitas, Carla Maria Dal Sasso ,</editor>
		<editor>Sarkar, Sudeep ,</editor>
		<editor>Scopigno, Roberto ,</editor>
		<editor>Silva, Luciano,</editor>
		<e-mailaddress>yomna.safaa-eldin@eng.asu.edu.eg</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)</conferencename>
		<conferencelocation>Ouro Preto, MG, Brazil</conferencelocation>
		<date>22-25 Aug. 2012</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>committee machines, Bayes, gender classification.</keywords>
		<abstract>This paper presents a novel method for combining the outputs of  different gender classification techniques based on facial images. Merging the methods is performed by a committee machine using the Bayesian theorem. We implement and compare several well-known individual classifiers on four different datasets, then we experiment the proposed machine, and show that it significantly improves the accuracy of classification compared to individual classifiers. We also include results that address the effect of scale on the performance of classifiers.</abstract>
		<language>en</language>
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