Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243711
Type: Artigo
Title: Deep representations for iris, face, and fingerprint spoofing detection
Author: Menotti, David
Chiachia
Giovani
Pinto, Allan
Schwartz
William Robson
Pedrini, Helio
Falcão, Alexandre Xavier
Rocha, Anderson
Abstract: Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks-each one containing real and fake samples of a given biometric modality and attack type- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.
Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofi
Subject: Aprendizado profundo
Redes neurais convolucionais
Ataques de falsificação de face
Identificação biométrica
Imagens digitais
Reconhecimento de padrões
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Citation: Deep Representations For Iris, Face, And Fingerprint Spoofing Detection. Ieee-inst Electrical Electronics Engineers Inc, v. 10, p. 864-879 APR-2015.
Rights: fechado
Fechado
Identifier DOI: 10.1109/TIFS.2015.2398817
Address: https://ieeexplore.ieee.org/abstract/document/7029061
Date Issue: 2015
Appears in Collections:IC - Artigos e Outros Documentos

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