Generative Convolutional Networks for Latent Fingerprint Reconstruction
This work addresses the challenge of improving fingerprint recognition accuracy for biometric systems, particularly in forensic or security applications, by enhancing latent fingerprints, though it appears incremental as it builds on existing methods.
The paper tackled the problem of enhancing corrupted or partially missing fingerprint images by applying generative convolutional networks to denoise minutiae and predict missing ridge patterns, resulting in improved fingerprint recognition performance when tested as a pre-processing step with standard feature extraction methods on multiple public datasets.
Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BOZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.