A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection
The use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9933421/ |
| _version_ | 1828098953426501632 |
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| author | Rodrigo Colnago Contreras Luis Gustavo Nonato Maurilio Boaventura Ines Aparecida Gasparotto Boaventura Francisco Lledo Dos Santos Rodrigo Bruno Zanin Monique Simplicio Viana |
| author_facet | Rodrigo Colnago Contreras Luis Gustavo Nonato Maurilio Boaventura Ines Aparecida Gasparotto Boaventura Francisco Lledo Dos Santos Rodrigo Bruno Zanin Monique Simplicio Viana |
| author_sort | Rodrigo Colnago Contreras |
| collection | DOAJ |
| description | The use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the fingerprint is the most considered due to the ease of collection, the uniqueness of each user, and the large amount of solid theories and computational libraries available in the scientific literature. However, in recent years, the falsification of these biometrics with synthetic materials, known as spoofing, has become a real threat to these systems. To circumvent these effects without the addition of hardware devices, techniques based on the analysis of texture pattern descriptors were developed. In this work, we propose a new framework based on steps of data augmentation, image processing and replication, and feature fusion and reduction. The method has as main objective to improve the ability of classifiers, or sets of classifiers, to recognize life in fingerprints. Furthermore, it is proposed a generalization of vector representation of patterns described in matrix form from the systematic use of sets of mapping functions. All the proposed material was analyzed on the well-established benchmark of the Liveness Detection competition of the 2009, 2011, 2013 and 2015 editions, presenting an average accuracy of 97.77% and being a competitive strategy in relation to the other techniques that make up the state of the art of specialized literature. |
| first_indexed | 2024-04-11T08:10:03Z |
| format | Article |
| id | doaj.art-225dea985acc4839988ee6141048d4cd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T08:10:03Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-225dea985acc4839988ee6141048d4cd2022-12-22T04:35:23ZengIEEEIEEE Access2169-35362022-01-011011768111770610.1109/ACCESS.2022.32183359933421A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness DetectionRodrigo Colnago Contreras0https://orcid.org/0000-0003-4003-7791Luis Gustavo Nonato1https://orcid.org/0000-0002-8514-8033Maurilio Boaventura2https://orcid.org/0000-0002-4292-8320Ines Aparecida Gasparotto Boaventura3Francisco Lledo Dos Santos4https://orcid.org/0000-0002-7718-8203Rodrigo Bruno Zanin5Monique Simplicio Viana6Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, São Paulo, BrazilInstitute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, São Paulo, BrazilInstitute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto, São Paulo, BrazilInstitute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto, São Paulo, BrazilFaculty of Architecture and Engineering, Mato Grosso State University, Cáceres, Mato Grosso, BrazilFaculty of Architecture and Engineering, Mato Grosso State University, Cáceres, Mato Grosso, BrazilComputing Department, Federal University of São Carlos, São Carlos, São Paulo, BrazilThe use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the fingerprint is the most considered due to the ease of collection, the uniqueness of each user, and the large amount of solid theories and computational libraries available in the scientific literature. However, in recent years, the falsification of these biometrics with synthetic materials, known as spoofing, has become a real threat to these systems. To circumvent these effects without the addition of hardware devices, techniques based on the analysis of texture pattern descriptors were developed. In this work, we propose a new framework based on steps of data augmentation, image processing and replication, and feature fusion and reduction. The method has as main objective to improve the ability of classifiers, or sets of classifiers, to recognize life in fingerprints. Furthermore, it is proposed a generalization of vector representation of patterns described in matrix form from the systematic use of sets of mapping functions. All the proposed material was analyzed on the well-established benchmark of the Liveness Detection competition of the 2009, 2011, 2013 and 2015 editions, presenting an average accuracy of 97.77% and being a competitive strategy in relation to the other techniques that make up the state of the art of specialized literature.https://ieeexplore.ieee.org/document/9933421/Fingerprint liveness detectionspoofing detectionpattern recognitiontexture analysiscomputer vision |
| spellingShingle | Rodrigo Colnago Contreras Luis Gustavo Nonato Maurilio Boaventura Ines Aparecida Gasparotto Boaventura Francisco Lledo Dos Santos Rodrigo Bruno Zanin Monique Simplicio Viana A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection IEEE Access Fingerprint liveness detection spoofing detection pattern recognition texture analysis computer vision |
| title | A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection |
| title_full | A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection |
| title_fullStr | A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection |
| title_full_unstemmed | A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection |
| title_short | A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection |
| title_sort | new multi filter framework for texture image representation improvement using set of pattern descriptors to fingerprint liveness detection |
| topic | Fingerprint liveness detection spoofing detection pattern recognition texture analysis computer vision |
| url | https://ieeexplore.ieee.org/document/9933421/ |
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