Multiple Instances Fingerprint Image Data Acquisition
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Date
2023
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International Conference on Electrical, Computer and Energy Technologies
Abstract
Using public fingerprint databases to validate the
multi-instance fusion approach of a Multi-instance Biometric
Authentication System (MBAS) for accurate authentication is a
means of overcoming some of the limitations of a Unimodal
Biometric System (UBS). Nevertheless, a significant portion of
the web databases used for MBAS (Machine Learning-Based
Automated Systems) were obtained under controlled conditions,
with the photos being curated to align with a certain algorithmic
objective. The performance of biometric systems exhibits
variability when the datasets used in the algorithms undergo
modifications as a result of discrepancies in the settings under
which the photos were obtained. In this study, a database
containing multiple fingerprint instances was developed locally
in an uncontrolled environment. The aim was to create a
database containing numerous examples of fingerprints for the
same person from one or multiple data collection sessions. The
database was produced locally by acquiring six samples of ten
fingerprint instances of 150 subjects in an uncontrolled
environment using a Futronic fingerprint scanner. The created
database and results will be helpful to all researchers in the
biometrics field as a tool for improving multi-instance fusion
methods and enabling an unbiased evaluation of algorithms.
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Citation
Janet O. Jooda, Roseline Oluwaseun Ogundokun, Oluyinka T. Adedeji, Sunday Adeola Ajagbe, Matthew O. Adigun, Alice O. Oke, Elijah O. Omidiora, (2023). Multiple Instances Fingerprint Image Data Acquisition, 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)