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		<doi>10.1109/SIBGRAPI.2015.16</doi>
		<citationkey>SilvaLuBaPeFaMe:2015:ApIrCo</citationkey>
		<title>An Approach to Iris Contact Lens Detection based on Deep Image Representations</title>
		<format>On-line</format>
		<year>2015</year>
		<numberoffiles>1</numberoffiles>
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		<author>Silva, Pedro,</author>
		<author>Luz, Eduardo,</author>
		<author>Baeta, Rafael,</author>
		<author>Pedrini, Helio,</author>
		<author>Falcao, Alexandre Xavier,</author>
		<author>Menotti, David,</author>
		<affiliation>Federal University of Ouro Preto</affiliation>
		<affiliation>Federal University of Ouro Preto</affiliation>
		<affiliation>Federal University of Ouro Preto</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>Federal University of Ouro Preto</affiliation>
		<editor>Papa, Joćo Paulo,</editor>
		<editor>Sander, Pedro Vieira,</editor>
		<editor>Marroquim, Ricardo Guerra,</editor>
		<editor>Farrell, Ryan,</editor>
		<e-mailaddress>menottid@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador, BA, Brazil</conferencelocation>
		<date>26-29 Aug. 2015</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>biometrics, contact lens detection, deep learning, convolutional networks.</keywords>
		<abstract>Spoofing detection is a challenging task in biometric systems, when differentiating illegitimate users from genuine ones. Although iris scans are far more inclusive than fingerprints, and also more precise for person authentication, iris recognition systems are vulnerable to spoofing via textured cosmetic contact lenses. Iris spoofing detection is also referred to as liveness detection (binary classification of fake and real images). In this work, we focus on a three-class detection problem: images with textured (colored) contact lenses, soft contact lenses, and no lenses. Our approach uses a convolutional network to build a deep image representation and an additional fully-connected single layer with softmax regression for classification. Experiments are conducted in comparison with a state-of-the-art approach (SOTA) on two public iris image databases for contact lens detection: 2013 Notre Dame and IIIT-Delhi. Our approach can achieve a 30% performance gain over SOTA on the former database (from 80% to 86%) and comparable results on the latter. Since IIIT-Delhi does not provide segmented iris images and, differently from SOTA, our approach does not segment the iris yet, we conclude that these are very promising results.</abstract>
		<language>en</language>
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		<usergroup>menottid@gmail.com</usergroup>
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