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		<identifier>8JMKD3MGPAW/3S39KCL</identifier>
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		<citationkey>BezerraGome:2018:ReOcLa</citationkey>
		<title>Recognition of occluded and lateral faces using MTCNN, Dlib and homographies</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>1</numberoffiles>
		<size>1307 KiB</size>
		<author>Bezerra, Gustavo Alves,</author>
		<author>Gomes, Rafael Beserra,</author>
		<affiliation>Universidade Federal do Rio Grande do Norte,</affiliation>
		<affiliation>Universidade Federal do Rio Grande do Norte,</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>gustavowl@lcc.ufrn.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<keywords>face recognition, occlusion, homography.</keywords>
		<abstract>With the advance of technology it is possible to create more robust security systems. For this task, image processing alongside Deep Neural Networks are currently being used in several works for facial recognition. However, occlusions and faces in different angles are a challenge for most algorithms. Attempting to contour this issue, an algorithm for facial recognition combining MTCNN, DLIB and homographies is proposed. In the obtained results, a comparison between the proposed algorithm and basis works indicates that, for some controlled cases, a mean accuracy improvement of 7.4% was obtained, with a maximum of 8.23% for occluded faces and 14.08% for lateral faces.</abstract>
		<language>en</language>
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