Imperial College London


Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition



m.bronstein Website




569Huxley BuildingSouth Kensington Campus






BibTex format

author = {Svoboda, J and Monti, F and Bronstein, MM},
doi = {10.1109/BTAS.2017.8272727},
pages = {429--436},
title = {Generative convolutional networks for latent fingerprint reconstruction},
url = {},
year = {2018}

RIS format (EndNote, RefMan)

AB - © 2017 IEEE. 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 BO-ZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.
AU - Svoboda,J
AU - Monti,F
AU - Bronstein,MM
DO - 10.1109/BTAS.2017.8272727
EP - 436
PY - 2018///
SP - 429
TI - Generative convolutional networks for latent fingerprint reconstruction
UR -
ER -