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		<site>sibgrapi.sid.inpe.br 802</site>
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		<citationkey>SilvaZavaBellSilv:2016:TrFaBa</citationkey>
		<title>Follow that nose: tracking faces based on the nose region and image quality feedback</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
		<size>3204 KiB</size>
		<author>Silva, Luan Porfírio e,</author>
		<author>Zavan, Flávio Henrique de Bittencourt,</author>
		<author>Bellon, Olga Regina Pereira,</author>
		<author>Silva, Luciano,</author>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>flavio@ufpr.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Face Processing Application Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Face tracker, Nose region, Face image quality.</keywords>
		<abstract>Face tracking uses temporal information to infer the position of the face in each frame. One of its applications is in unconstrained (in-the-wild) environments where face detection methods fail to perform robustly. Current approaches presented in the literature are based on facial landmarks. Therefore, they have limitations when applied in in-the-wild environments as estimating the landmarks in such scenarios is not trivial. To address this issue, we propose a novel landmark-free approach based on a state-of-the-art generic visual tracking method, as baseline, combined with face quality assessment for initializing the tracking. In addition, we introduce using only the nose region as a solution for in-the-wild face tracking, initializing it with the nose of the best quality face in the video sequence. The nose is detected and used to estimate the head pose, which is combined with the face quality score for choosing the initialization frame. The nose region, rather than the entire face was chosen due to it being unlikely to be occluded, mostly invariant to facial expressions and visible in a long range of head poses. We performed experiments on the 300 Videos in the Wild dataset and our results favorably compared against the baseline method.</abstract>
		<language>en</language>
		<targetfile>Follow_Sibgrapi_Noses.pdf</targetfile>
		<usergroup>flavio@ufpr.br</usergroup>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2016/09.13.14.16</url>
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