Fingerprint recognition
Latent fingerprint identification is crucial in forensic science for linking suspects to crime scenes. Latent examiners obtain unique, reliable evidence by revealing hidden prints through advanced techniques. However, latent fingerprints often are partial prints with undesirable characteristics such as noise or distortion. Due to these characteristics, identifying the physical details of a latent fingerprint, known as minutiae, is a complex task. Recent publications found that there are subsets on one minutia in latent fingerprints that, when removed, increase the matching score.
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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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