Ramachandra, Raghavendra ; Venkatesh, Sushma ; Damer, Naser ; Vetrekar, Narayan ; Gad, R. S. (2024)
Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study.
2024 IEEE Winter Conference on Applications of Computer Vision. Waikoloa, USA (04.01.-08.01.2024)
doi: 10.1109/WACV57701.2024.00607
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multi-spectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Ramachandra, Raghavendra ; Venkatesh, Sushma ; Damer, Naser ; Vetrekar, Narayan ; Gad, R. S. |
| Art des Eintrags: | Bibliographie |
| Titel: | Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study |
| Sprache: | Englisch |
| Publikationsjahr: | 13 April 2024 |
| Verlag: | IEEE |
| Buchtitel: | Proceedings: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision |
| Veranstaltungstitel: | 2024 IEEE Winter Conference on Applications of Computer Vision |
| Veranstaltungsort: | Waikoloa, USA |
| Veranstaltungsdatum: | 04.01.-08.01.2024 |
| DOI: | 10.1109/WACV57701.2024.00607 |
| Kurzbeschreibung (Abstract): | Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multi-spectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images. |
| Freie Schlagworte: | Biometrics, Face recognition, Deep learning, Morphing, Morphing attack |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 08 Mai 2024 06:27 |
| Letzte Änderung: | 08 Mai 2024 06:27 |
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