Abstract
During the past decade, hyperspectral imaging (HSI) has been an area of broad, innovative work in a variety of applications such as health, defense, and remote sensing. Hyperspectral images can be collected using a compact HSI imager and are referred to also as hypercubes. Currently, there are no biometric hyperspectral databases available to the community. In this paper, we create the Finger Hypercubes Sanitization with Demographics Database (FHSD) (https://github.com/cysber-CSIS/GMU-CSIS—Finger-Hypercubes-Sanitization-with-Demographics-FHSD-2022) consisting of hyperspectral images of human fingers along with their demographics (i.e., age, gender, and ethnicity) captured before and after hand sanitization. This gender-balanced database consists of images pertaining to 100 subjects collected in an indoor environment with a white background under proper lighting conditions using the Resonon bench-top Pika-L hyperspectral imaging system (400–1000 nm). For each subject, multiple left and right index samples were acquired before and after sanitization. In addition to spatial information, HSI data provides 281 channels decoding a spectral component able to describe skin reflectance. Thus, this data holds great potential for enabling a more in-depth analysis of demographic differentials in fingerprints compared to conventional sensing technologies.
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This work was funded by the National Science Foundation (NSF) grant #2036151.
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Sumanth, S.S., Marasco, E. (2023). A Novel Time-Series Database of Finger Hypercubes Before and After Hand Sanitization with Demographics. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_43
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