Eintrag weiter verarbeiten
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection
Gespeichert in:
| Zeitschriftentitel: | Journal of Artificial Intelligence and Soft Computing Research |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Journal of Artificial Intelligence and Soft Computing Research, 9, 2019, 1, S. 41-49 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
de Souza, Gustavo Botelho Santos, Daniel Felipe da Silva Pires, Rafael Gonçalves Marana, Aparecido Nilceu Papa, João Paulo de Souza, Gustavo Botelho Santos, Daniel Felipe da Silva Pires, Rafael Gonçalves Marana, Aparecido Nilceu Papa, João Paulo |
|---|---|
| author |
de Souza, Gustavo Botelho Santos, Daniel Felipe da Silva Pires, Rafael Gonçalves Marana, Aparecido Nilceu Papa, João Paulo |
| spellingShingle |
de Souza, Gustavo Botelho Santos, Daniel Felipe da Silva Pires, Rafael Gonçalves Marana, Aparecido Nilceu Papa, João Paulo Journal of Artificial Intelligence and Soft Computing Research Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems |
| author_sort |
de souza, gustavo botelho |
| spelling |
de Souza, Gustavo Botelho Santos, Daniel Felipe da Silva Pires, Rafael Gonçalves Marana, Aparecido Nilceu Papa, João Paulo 2083-2567 Walter de Gruyter GmbH Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems http://dx.doi.org/10.2478/jaiscr-2018-0023 <jats:title>Abstract</jats:title> <jats:p>Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.</jats:p> Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection Journal of Artificial Intelligence and Soft Computing Research |
| doi_str_mv |
10.2478/jaiscr-2018-0023 |
| facet_avail |
Online |
| finc_class_facet |
Informatik Technik |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMjQ3OC9qYWlzY3ItMjAxOC0wMDIz |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMjQ3OC9qYWlzY3ItMjAxOC0wMDIz |
| institution |
DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 DE-105 DE-14 DE-Ch1 DE-L229 |
| imprint |
Walter de Gruyter GmbH, 2019 |
| imprint_str_mv |
Walter de Gruyter GmbH, 2019 |
| issn |
2083-2567 |
| issn_str_mv |
2083-2567 |
| language |
English |
| mega_collection |
Walter de Gruyter GmbH (CrossRef) |
| match_str |
desouza2019deepfeaturesextractionforrobustfingerprintspoofingattackdetection |
| publishDateSort |
2019 |
| publisher |
Walter de Gruyter GmbH |
| recordtype |
ai |
| record_format |
ai |
| series |
Journal of Artificial Intelligence and Soft Computing Research |
| source_id |
49 |
| title |
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_unstemmed |
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_full |
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_fullStr |
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_full_unstemmed |
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_short |
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_sort |
deep features extraction for robust fingerprint spoofing attack detection |
| topic |
Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems |
| url |
http://dx.doi.org/10.2478/jaiscr-2018-0023 |
| publishDate |
2019 |
| physical |
41-49 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.</jats:p> |
| container_issue |
1 |
| container_start_page |
41 |
| container_title |
Journal of Artificial Intelligence and Soft Computing Research |
| container_volume |
9 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792345828647501830 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T17:29:42.349Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Deep+Features+Extraction+for+Robust+Fingerprint+Spoofing+Attack+Detection&rft.date=2019-01-01&genre=article&issn=2083-2567&volume=9&issue=1&spage=41&epage=49&pages=41-49&jtitle=Journal+of+Artificial+Intelligence+and+Soft+Computing+Research&atitle=Deep+Features+Extraction+for+Robust+Fingerprint+Spoofing+Attack+Detection&aulast=Papa&aufirst=Jo%C3%A3o+Paulo&rft_id=info%3Adoi%2F10.2478%2Fjaiscr-2018-0023&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792345828647501830 |
| author | de Souza, Gustavo Botelho, Santos, Daniel Felipe da Silva, Pires, Rafael Gonçalves, Marana, Aparecido Nilceu, Papa, João Paulo |
| author_facet | de Souza, Gustavo Botelho, Santos, Daniel Felipe da Silva, Pires, Rafael Gonçalves, Marana, Aparecido Nilceu, Papa, João Paulo, de Souza, Gustavo Botelho, Santos, Daniel Felipe da Silva, Pires, Rafael Gonçalves, Marana, Aparecido Nilceu, Papa, João Paulo |
| author_sort | de souza, gustavo botelho |
| container_issue | 1 |
| container_start_page | 41 |
| container_title | Journal of Artificial Intelligence and Soft Computing Research |
| container_volume | 9 |
| description | <jats:title>Abstract</jats:title> <jats:p>Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.</jats:p> |
| doi_str_mv | 10.2478/jaiscr-2018-0023 |
| facet_avail | Online |
| finc_class_facet | Informatik, Technik |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMjQ3OC9qYWlzY3ItMjAxOC0wMDIz |
| imprint | Walter de Gruyter GmbH, 2019 |
| imprint_str_mv | Walter de Gruyter GmbH, 2019 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 2083-2567 |
| issn_str_mv | 2083-2567 |
| language | English |
| last_indexed | 2024-03-01T17:29:42.349Z |
| match_str | desouza2019deepfeaturesextractionforrobustfingerprintspoofingattackdetection |
| mega_collection | Walter de Gruyter GmbH (CrossRef) |
| physical | 41-49 |
| publishDate | 2019 |
| publishDateSort | 2019 |
| publisher | Walter de Gruyter GmbH |
| record_format | ai |
| recordtype | ai |
| series | Journal of Artificial Intelligence and Soft Computing Research |
| source_id | 49 |
| spelling | de Souza, Gustavo Botelho Santos, Daniel Felipe da Silva Pires, Rafael Gonçalves Marana, Aparecido Nilceu Papa, João Paulo 2083-2567 Walter de Gruyter GmbH Artificial Intelligence Computer Vision and Pattern Recognition Hardware and Architecture Modeling and Simulation Information Systems http://dx.doi.org/10.2478/jaiscr-2018-0023 <jats:title>Abstract</jats:title> <jats:p>Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.</jats:p> Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection Journal of Artificial Intelligence and Soft Computing Research |
| spellingShingle | de Souza, Gustavo Botelho, Santos, Daniel Felipe da Silva, Pires, Rafael Gonçalves, Marana, Aparecido Nilceu, Papa, João Paulo, Journal of Artificial Intelligence and Soft Computing Research, Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Modeling and Simulation, Information Systems |
| title | Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_full | Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_fullStr | Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_full_unstemmed | Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_short | Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| title_sort | deep features extraction for robust fingerprint spoofing attack detection |
| title_unstemmed | Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection |
| topic | Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Modeling and Simulation, Information Systems |
| url | http://dx.doi.org/10.2478/jaiscr-2018-0023 |