<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<holdercode>{ibi 8JMKD3MGPEW34M/46T9EHH}</holdercode>
		<identifier>6qtX3pFwXQZG2LgkFdY/R2tfw</identifier>
		<repository>sid.inpe.br/sibgrapi@80/2007/08.02.10.54</repository>
		<lastupdate>2007:08.02.10.54.44 sid.inpe.br/banon/2001/03.30.15.38 administrator</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi@80/2007/08.02.10.54.45</metadatarepository>
		<metadatalastupdate>2022:06.14.00.13.37 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2007}</metadatalastupdate>
		<doi>10.1109/SIBGRAPI.2007.44</doi>
		<citationkey>LeitePeGoVeSaNaCa:2007:LeEyDe</citationkey>
		<title>A learning-based eye detector coupled with eye candidate filtering and PCA features</title>
		<format>Printed, On-line.</format>
		<year>2007</year>
		<numberoffiles>1</numberoffiles>
		<size>181 KiB</size>
		<author>Leite, Bruno de Brito,</author>
		<author>Pereira, Eanes Torres,</author>
		<author>Gomes, Herman Martins,</author>
		<author>Veloso, Luciana Ribeiro,</author>
		<author>Santos, C´&#305,</author>
		<author>Nascimento, cero Einstein do,</author>
		<author>de Carvalho, João Marques,</author>
		<affiliation>Departamento de Sistemas e Computação, Universidade Federal de Campina Grande</affiliation>
		<affiliation>Departamento de Sistemas e Computação, Universidade Federal de Campina Grande</affiliation>
		<affiliation>Departamento de Sistemas e Computação, Universidade Federal de Campina Grande</affiliation>
		<affiliation>Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande</affiliation>
		<affiliation>Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande</affiliation>
		<affiliation>Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande</affiliation>
		<editor>Falcão, Alexandre Xavier,</editor>
		<editor>Lopes, Hélio Côrtes Vieira,</editor>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<conferencelocation>Belo Horizonte, MG, Brazil</conferencelocation>
		<date>7-10 Oct. 2007</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>eye detection, neural networks, principal component analysis, integral image.</keywords>
		<abstract>In this work, we present a system based on a Neural Network classifier for eye detection in human face images. This classifier works on eye candidate regions extracted from a face image and represented by a reduced number of features, selected by Principal Component Analysis. The regions are determined considering that in an image window containing the eye, the gray level distribution will generally assume a pattern of adjacent light-dark-light horizontal and vertical stripes, corresponding to the eyelid, pupil and eyelid, respectively. For training, validation and testing, a database was built with a total of 4,400 images. Experimental results have shown that the proposed approach correctly detects more eyes than any of two existing systems (Rowley-Baluja-Kanade and Machine Perception Toolbox), for eye location error tolerances from 0 to 5 pixels. Considering an error tolerance of 9 pixels, the correct detection rate achieved was above 90%. .</abstract>
		<language>en</language>
		<targetfile>gomes-EyeDetection.pdf</targetfile>
		<usergroup>hmg@dsc.ufcg.edu.br administrator</usergroup>
		<visibility>shown</visibility>
		<nexthigherunit>8JMKD3MGPEW34M/46SF8Q5</nexthigherunit>
		<nexthigherunit>8JMKD3MGPEW34M/4742MCS</nexthigherunit>
		<citingitemlist>sid.inpe.br/sibgrapi/2022/05.14.00.14 3</citingitemlist>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi@80/2007/08.02.10.54</url>
	</metadata>
</metadatalist>