On the vulnerability of extended Multispectral face recognition systems towards presentation attacks

Presentation attacks (a.k.a, direct attacks or spoofing attacks) against face recognition systems have emerged as a serious security threat. To mitigate these attacks on conventional face recognition systems, several Presentation Attack Detection (PAD) algorithms have been developed, which address various Presentation Attack Instruments (PAI) including 3D face masks, 2D photo, wrap photo and electronic display, that can be used for the attack. In this paper, we demonstrate and evaluate the vulnerability of an extended Multispectral face recognition system. The extended Multispectral system captures the face image across various spectral bands, thus, we propose to study each of these spectral bands for the vulnerability towards presentation attacks. We have employed a commercial Multispectral camera - SpectraCam™ that can capture seven different spectral bands to collect both bona-fide (a.k.a, live or normal or real) samples as well as artefact (or spoof) face samples. Extensive experiments are carried out on the newly compiled database to provide insights on the vulnerability of the extended Multispectral face system towards PAI generated using a printer. We have created the face artefacts using two different printers, which include laser and inkjet printers. Further, we have also evaluated the state-of-the-art PAD algorithms that are widely employed in conventional face PAD systems. Our study reveals the vulnerability of extended Multispectral face recognition system with respect to the print attack. The results obtained using state-of-the-art PAD algorithms further indicate the challenge to detect the presentation attacks in extended Multispectral face recognition systems.