Feature Fusion for Fingerprint Liveness Detection: A Comparative Study

Autores
Toosi, Amirhosein; Bottino, Andrea; Cumani, Sandro; Negri, Pablo Augusto; Sottile, Pietro Luca
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Spoofing attacks carried out using artificial replicas are a severe threat for fingerprint-based biometric systems and, thus, require the development of effective countermeasures. One possible protection method is to implement software modules that analyze fingerprint images to tell live from fake samples. Most of the static software-based approaches in the literature are based on various image features, each with its own strengths, weaknesses, and discriminative power. Such features can be seen as different and often complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. This paper aims at assessing the potential of these feature fusion approaches in the area of fingerprint liveness detection by analyzing different features and different methods for their aggregation. Experiments on publicly available benchmarks show the effectiveness of feature fusion methods, which improve the accuracy of those based on individual features and are competitive with respect to the alternative methods, such as the ones based on convolutional neural networks.
Fil: Toosi, Amirhosein. Politecnico di Torino; Italia
Fil: Bottino, Andrea. Politecnico di Torino; Italia
Fil: Cumani, Sandro. Politecnico di Torino; Italia
Fil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Sottile, Pietro Luca. Politecnico di Torino; Italia
Materia
BIOMETRIC COUNTERSPOOFING METHODS
FEATURE FUSION
FINGERPRINT LIVENESS DETECTION
LOCAL IMAGE FEATURES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/74263
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Feature Fusion for Fingerprint Liveness Detection: A Comparative StudyToosi, AmirhoseinBottino, AndreaCumani, SandroNegri, Pablo AugustoSottile, Pietro LucaBIOMETRIC COUNTERSPOOFING METHODSFEATURE FUSIONFINGERPRINT LIVENESS DETECTIONLOCAL IMAGE FEATUREShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Spoofing attacks carried out using artificial replicas are a severe threat for fingerprint-based biometric systems and, thus, require the development of effective countermeasures. One possible protection method is to implement software modules that analyze fingerprint images to tell live from fake samples. Most of the static software-based approaches in the literature are based on various image features, each with its own strengths, weaknesses, and discriminative power. Such features can be seen as different and often complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. This paper aims at assessing the potential of these feature fusion approaches in the area of fingerprint liveness detection by analyzing different features and different methods for their aggregation. Experiments on publicly available benchmarks show the effectiveness of feature fusion methods, which improve the accuracy of those based on individual features and are competitive with respect to the alternative methods, such as the ones based on convolutional neural networks.Fil: Toosi, Amirhosein. Politecnico di Torino; ItaliaFil: Bottino, Andrea. Politecnico di Torino; ItaliaFil: Cumani, Sandro. Politecnico di Torino; ItaliaFil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Sottile, Pietro Luca. Politecnico di Torino; ItaliaInstitute of Electrical and Electronics Engineers Inc.2017-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/74263Toosi, Amirhosein; Bottino, Andrea; Cumani, Sandro; Negri, Pablo Augusto; Sottile, Pietro Luca; Feature Fusion for Fingerprint Liveness Detection: A Comparative Study; Institute of Electrical and Electronics Engineers Inc.; IEEE Access; 5; 10-2017; 23695-237092169-3536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2017.2763419info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/8068202/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:05Zoai:ri.conicet.gov.ar:11336/74263instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:46:05.915CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
title Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
spellingShingle Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
Toosi, Amirhosein
BIOMETRIC COUNTERSPOOFING METHODS
FEATURE FUSION
FINGERPRINT LIVENESS DETECTION
LOCAL IMAGE FEATURES
title_short Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
title_full Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
title_fullStr Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
title_full_unstemmed Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
title_sort Feature Fusion for Fingerprint Liveness Detection: A Comparative Study
dc.creator.none.fl_str_mv Toosi, Amirhosein
Bottino, Andrea
Cumani, Sandro
Negri, Pablo Augusto
Sottile, Pietro Luca
author Toosi, Amirhosein
author_facet Toosi, Amirhosein
Bottino, Andrea
Cumani, Sandro
Negri, Pablo Augusto
Sottile, Pietro Luca
author_role author
author2 Bottino, Andrea
Cumani, Sandro
Negri, Pablo Augusto
Sottile, Pietro Luca
author2_role author
author
author
author
dc.subject.none.fl_str_mv BIOMETRIC COUNTERSPOOFING METHODS
FEATURE FUSION
FINGERPRINT LIVENESS DETECTION
LOCAL IMAGE FEATURES
topic BIOMETRIC COUNTERSPOOFING METHODS
FEATURE FUSION
FINGERPRINT LIVENESS DETECTION
LOCAL IMAGE FEATURES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Spoofing attacks carried out using artificial replicas are a severe threat for fingerprint-based biometric systems and, thus, require the development of effective countermeasures. One possible protection method is to implement software modules that analyze fingerprint images to tell live from fake samples. Most of the static software-based approaches in the literature are based on various image features, each with its own strengths, weaknesses, and discriminative power. Such features can be seen as different and often complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. This paper aims at assessing the potential of these feature fusion approaches in the area of fingerprint liveness detection by analyzing different features and different methods for their aggregation. Experiments on publicly available benchmarks show the effectiveness of feature fusion methods, which improve the accuracy of those based on individual features and are competitive with respect to the alternative methods, such as the ones based on convolutional neural networks.
Fil: Toosi, Amirhosein. Politecnico di Torino; Italia
Fil: Bottino, Andrea. Politecnico di Torino; Italia
Fil: Cumani, Sandro. Politecnico di Torino; Italia
Fil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Sottile, Pietro Luca. Politecnico di Torino; Italia
description Spoofing attacks carried out using artificial replicas are a severe threat for fingerprint-based biometric systems and, thus, require the development of effective countermeasures. One possible protection method is to implement software modules that analyze fingerprint images to tell live from fake samples. Most of the static software-based approaches in the literature are based on various image features, each with its own strengths, weaknesses, and discriminative power. Such features can be seen as different and often complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. This paper aims at assessing the potential of these feature fusion approaches in the area of fingerprint liveness detection by analyzing different features and different methods for their aggregation. Experiments on publicly available benchmarks show the effectiveness of feature fusion methods, which improve the accuracy of those based on individual features and are competitive with respect to the alternative methods, such as the ones based on convolutional neural networks.
publishDate 2017
dc.date.none.fl_str_mv 2017-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/74263
Toosi, Amirhosein; Bottino, Andrea; Cumani, Sandro; Negri, Pablo Augusto; Sottile, Pietro Luca; Feature Fusion for Fingerprint Liveness Detection: A Comparative Study; Institute of Electrical and Electronics Engineers Inc.; IEEE Access; 5; 10-2017; 23695-23709
2169-3536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/74263
identifier_str_mv Toosi, Amirhosein; Bottino, Andrea; Cumani, Sandro; Negri, Pablo Augusto; Sottile, Pietro Luca; Feature Fusion for Fingerprint Liveness Detection: A Comparative Study; Institute of Electrical and Electronics Engineers Inc.; IEEE Access; 5; 10-2017; 23695-23709
2169-3536
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2017.2763419
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/8068202/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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