Generative Convolutional Networks for Latent Fingerprint Reconstruction
2017 IEEE International Joint Conference on Biometrics (IJCB)(2017)
摘要
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.
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关键词
publicly available latent fingerprint datasets,standard feature extraction methods,visible minutiae,fingerprint images,negative detection rates,false positive detection rates,fingerprint ridge pattern,minutiae points,fingerprint recognition,latent fingerprint reconstruction,generative convolutional networks,BO
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