Does complimentary information from multispectral imaging improve face presentation attack detection?
This work addresses face spoofing attacks for security applications, but it is incremental as it builds on existing multispectral imaging approaches.
The paper tackled face presentation attack detection by using multispectral imaging to complement visible spectrum data, achieving superior performance through score and image fusion methods on a new database of 61,650 samples.
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.