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		<identifier>8JMKD3MGPAW/3PK84H8</identifier>
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		<lastupdate>2017:09.11.13.40.19 sid.inpe.br/banon/2001/03.30.15.38 flaviozavan@gmail.com</lastupdate>
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		<citationkey>ZavanSilvBell:2017:NoPoEs</citationkey>
		<title>Nose pose estimation in the wild and its applications on nose tracking and 3D face alignment</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>6056 KiB</size>
		<author>Zavan, Flávio Henrique de Bittencourt,</author>
		<author>Silva, Luciano,</author>
		<author>Bellon, Olga Regina Pereira,</author>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>flaviozavan@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>face processing, face analysis, head pose estimation.</keywords>
		<abstract>An automatic, landmark free SVM-based method for head pose estimation, solely using the nose region, in constrained and unconstrained scenarios, is presented. Using the nose region has advantages over the whole face; it is less likely to be occluded or deformed by facial expressions, and is proven to be highly discriminant in all poses from profile to frontal. The approach, SVM-NosePose, receives a nose region as and classifies it into a discrete set of poses. Estimation favorably compares against state-of-the-art works on six publicly available datasets. Three applications are derived from the proposed methodology: 1) the original inclusion of a head pose score for face quality estimation for initializing a nose tracker, leading to higher accuracy; 2) 3D face alignment in the wild using only the nose pose, enabling consistent estimates even in challenging scenarios; and 3) multipose action unit detection and intensity estimation for facial images in the wild.</abstract>
		<language>en</language>
		<targetfile>wtd_sibgrapi_2017_camera_ready.pdf</targetfile>
		<usergroup>flaviozavan@gmail.com</usergroup>
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