2 research outputs found
Does complimentary information from multispectral imaging improve face presentation attack detection?
Presentation Attack Detection (PAD) has been extensively studied,
particularly in the visible spectrum. With the advancement of sensing
technology beyond the visible range, multispectral imaging has gained
significant attention in this direction. We present PAD based on multispectral
images constructed for eight different presentation artifacts resulted from
three different artifact species. In this work, we introduce Face Presentation
Attack Multispectral (FPAMS) database to demonstrate the significance of
employing multispectral imaging. The goal of this work is to study
complementary information that can be combined in two different ways (image
fusion and score fusion) from multispectral imaging to improve the face PAD.
The experimental evaluation results present an extensive qualitative analysis
of 61650 sample multispectral images collected for bonafide and artifacts. The
PAD based on the score fusion and image fusion method presents superior
performance, demonstrating the significance of employing multispectral imaging
to detect presentation artifacts.Comment: Accepted in International IEEE Applied Sensing Conference (IEEE
APSCON) 202
Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study
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 multispectral D-MAD framework
introduce a multispectral image captured as a trusted capture to capture seven
different spectral bands to detect morphing attacks. Extensive experiments were
conducted on the newly created datasets 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.Comment: Under Revie