Evaluating Software-based Fingerprint Liveness Detection Using Convolutional Networks And Local Binary Patterns

dc.creatorFrassetto Nogueira R.
dc.creatorDe Alencar Lotufo R.
dc.creatorCampos Machado R.
dc.date2014
dc.date2015-06-25T17:59:17Z
dc.date2015-11-26T14:58:13Z
dc.date2015-06-25T17:59:17Z
dc.date2015-11-26T14:58:13Z
dc.date.accessioned2018-03-28T22:09:57Z
dc.date.available2018-03-28T22:09:57Z
dc.identifier9781479951758
dc.identifierBioms 2014 - 2014 Ieee Workshop On Biometric Measurements And Systems For Security And Medical Applications, Proceedings. Institute Of Electrical And Electronics Engineers Inc., v. , n. , p. 22 - 29, 2014.
dc.identifier
dc.identifier10.1109/BIOMS.2014.6951531
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84915768198&partnerID=40&md5=3e47c5c3fd7e07908f39ed5deee1d852
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87339
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87339
dc.identifier2-s2.0-84915768198
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1255815
dc.descriptionWith the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint liveness detection: Convolutional Networks with random weights and Local Binary Patterns. Both techniques were used in conjunction with a Support Vector Machine (SVM) classifier. Dataset Augmentation was used to increase classifier's performance and a variety of preprocessing operations were tested, such as frequency filtering, contrast equalization, and region of interest filtering. The experiments were made on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013, which comprise almost 50,000 real and fake fingerprints' images. Our best method achieves an overall rate of 95.2% of correctly classified samples - an improvement of 35% in test error when compared with the best previously published results.
dc.description
dc.description
dc.description22
dc.description29
dc.descriptionIEEE Italy Section
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dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationBIOMS 2014 - 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, Proceedings
dc.rightsfechado
dc.sourceScopus
dc.titleEvaluating Software-based Fingerprint Liveness Detection Using Convolutional Networks And Local Binary Patterns
dc.typeActas de congresos


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