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First Name: 
Maohsiu
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HSU

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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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Fingerprint recognition is crucial for device and data

security, especially with the widespread use of capacitive sensors

in mobile devices. However, denoising wet fingerprints from

these sensors poses challenges due to small fingerprint areas,

limited features, and significant moisture-induced dark regions.

Our ”DRB-FD” method combines a Featured Discriminator (FD)

and a Deformed Residual Block (DRB) with attention mechanisms,

drop-out layers, and pre-activation. In experiments using the Nasic9395

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