Real-world dataset
The data used in this study was gathered from various practical applications, specifically sourced from an access control device that incorporates a hardware module. Meanwhile, the data acquired through sampling conducted under 850nm light. The hardware device itself was utilized in diverse scenarios, encompassing indoor and outdoor environments, with and without background influences. Users participated in the data collection process by placing their palms naturally onto the device for multiple captures.
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Abstract—Fingerprint recognition technology has become
popular for mobile device authentication systems due to its
reliability and ease of use. As smartphones evolve, fingerprint
sensors are now integrated into smartphone power with a width
of 2.2 mm. However, tiny sensor sizes have led to limited finger
coverage and external factors such as sweat or water droplets
can cause image distortion, making user authentication more
challenging.
To address these issues, we propose the FFP-UNet, which uses
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