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Computer Science > Computer Vision and Pattern Recognition

arXiv:2311.11566 (cs)
[Submitted on 20 Nov 2023]

Title:Does complimentary information from multispectral imaging improve face presentation attack detection?

Authors:Narayan Vetrekar, Raghavendra Ramachandra, Sushma Venkatesh, Jyoti D. Pawar, R. S. Gad
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Abstract: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.
Comments: Accepted in International IEEE Applied Sensing Conference (IEEE APSCON) 2024
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2311.11566 [cs.CV]
  (or arXiv:2311.11566v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2311.11566

Submission history

From: Narayan Vetrekar [view email]
[v1] Mon, 20 Nov 2023 07:04:46 UTC (308 KB)
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