Optimized Deformed Residual Neural Network Accompanied by a Featured Discriminator for Wet and Fine-Grained Fingerprint Sensor Image

Fingerprint recognition is crucial for device and datasecurity, especially with the widespread use of capacitive sensorsin mobile devices. However, denoising wet fingerprints fromthese 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 Nasic93950606 aug dataset , DRB-FD achieved a remarkable 73.1%improvement, with the False Rejection Rate (FRR) decreasing by25% compared to the baseline PGT-Net. Both FD and DRB significantlycontributed, showing improvements of 47.4% (FRR decreasedby 16.2%) and 48.9% (FRR decreased by 8.8%), respectively.Besides achieving better FRR results, there is a significant increase in the number of fingerprint points successfullymatched after repair by the DRB-FD model.In conclusion, our innovative approach effectively denoises wet fingerprints from capacitive sensors, enhancing theaccuracy and reliability of fingerprint recognition systems.