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		<citationkey>LeitePeGoVeSaNaCa:2007:LeEyDe</citationkey>
		<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>
		<title>A learning-based eye detector coupled with eye candidate filtering and PCA features</title>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<year>2007</year>
		<editor>Falcão, Alexandre Xavier,</editor>
		<editor>Lopes, Hélio Côrtes Vieira,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 7-10, 2007</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<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>
		<tertiarytype>Full Paper</tertiarytype>
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