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.

利用生成卷积网络对指纹进行去噪和缺失图案预测的方法，作为指纹识别前的预处理步骤，能够显著降低 false positive 和 false negative 检测率，并可与多种标准特征提取方法结合使用，如 MINDTCT，MCC，BOZORTH3 等，本方法在使用不同传感器捕获的多个公开指纹数据集上进行了测试。