Pixel‐wise supervision for presentation attack detection on identity document cards
Abstract Identity documents (or IDs) play an important role in verifying the identity of a person with wide applications in banks, travel, video‐identification services and border controls. Replay or photocopied ID cards can be misused to pass ID control in unsupervised scenarios if the liveness of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-09-01
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| Series: | IET Biometrics |
| Online Access: | https://doi.org/10.1049/bme2.12088 |
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| _version_ | 1832559616274202624 |
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| author | Raghavendra Mudgalgundurao Patrick Schuch Kiran Raja Raghavendra Ramachandra Naser Damer |
| author_facet | Raghavendra Mudgalgundurao Patrick Schuch Kiran Raja Raghavendra Ramachandra Naser Damer |
| author_sort | Raghavendra Mudgalgundurao |
| collection | DOAJ |
| description | Abstract Identity documents (or IDs) play an important role in verifying the identity of a person with wide applications in banks, travel, video‐identification services and border controls. Replay or photocopied ID cards can be misused to pass ID control in unsupervised scenarios if the liveness of a person is not checked. To detect such presentation attacks on ID card verification process when presented virtually is a critical step for the biometric systems to assure authenticity. In this paper, a pixel‐wise supervision on DenseNet is proposed to detect presentation attacks of the printed and digitally replayed attacks. The authors motivate the approach to use pixel‐wise supervision to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in‐house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. It is demonstrated that the proposed approach achieves better performance compared to handcrafted features and Deep Models with an Equal Error Rate of 2.22% and Bona fide Presentation Classification Error Rate (BPCER) of 1.83% and 1.67% at Attack Presentation Classification Error Rate of 5% and 10%. |
| format | Article |
| id | doaj-art-3bf9b73c00754f089513571671c46292 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-3bf9b73c00754f089513571671c462922025-02-03T01:29:39ZengWileyIET Biometrics2047-49382047-49462022-09-0111538339510.1049/bme2.12088Pixel‐wise supervision for presentation attack detection on identity document cardsRaghavendra Mudgalgundurao0Patrick Schuch1Kiran Raja2Raghavendra Ramachandra3Naser Damer4Nect GmbH Hamburg GermanyNect GmbH Hamburg GermanyNTNU ‐ Gjøvik Gjøvik NorwayNTNU ‐ Gjøvik Gjøvik NorwayFraunhofer IGD Darmstadt GermanyAbstract Identity documents (or IDs) play an important role in verifying the identity of a person with wide applications in banks, travel, video‐identification services and border controls. Replay or photocopied ID cards can be misused to pass ID control in unsupervised scenarios if the liveness of a person is not checked. To detect such presentation attacks on ID card verification process when presented virtually is a critical step for the biometric systems to assure authenticity. In this paper, a pixel‐wise supervision on DenseNet is proposed to detect presentation attacks of the printed and digitally replayed attacks. The authors motivate the approach to use pixel‐wise supervision to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in‐house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. It is demonstrated that the proposed approach achieves better performance compared to handcrafted features and Deep Models with an Equal Error Rate of 2.22% and Bona fide Presentation Classification Error Rate (BPCER) of 1.83% and 1.67% at Attack Presentation Classification Error Rate of 5% and 10%.https://doi.org/10.1049/bme2.12088 |
| spellingShingle | Raghavendra Mudgalgundurao Patrick Schuch Kiran Raja Raghavendra Ramachandra Naser Damer Pixel‐wise supervision for presentation attack detection on identity document cards IET Biometrics |
| title | Pixel‐wise supervision for presentation attack detection on identity document cards |
| title_full | Pixel‐wise supervision for presentation attack detection on identity document cards |
| title_fullStr | Pixel‐wise supervision for presentation attack detection on identity document cards |
| title_full_unstemmed | Pixel‐wise supervision for presentation attack detection on identity document cards |
| title_short | Pixel‐wise supervision for presentation attack detection on identity document cards |
| title_sort | pixel wise supervision for presentation attack detection on identity document cards |
| url | https://doi.org/10.1049/bme2.12088 |
| work_keys_str_mv | AT raghavendramudgalgundurao pixelwisesupervisionforpresentationattackdetectiononidentitydocumentcards AT patrickschuch pixelwisesupervisionforpresentationattackdetectiononidentitydocumentcards AT kiranraja pixelwisesupervisionforpresentationattackdetectiononidentitydocumentcards AT raghavendraramachandra pixelwisesupervisionforpresentationattackdetectiononidentitydocumentcards AT naserdamer pixelwisesupervisionforpresentationattackdetectiononidentitydocumentcards |