  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.
