OrieNet: A Regression System for Latent Fingerprint Orientation Field Extraction

Orientation field is an important characteristic of fingerprints. Many biometrics processing steps rely on its accurate estimation. Previous works on this task failed because of blurry fingerprint patterns and severe background noises. In this paper, a new algorithm system specific for fingerprint orientation estimation is proposed, combining domain knowledge of handcraft methods and the generalization ability of DNN. System’s preprocessing part roughly extracts effective information of input image with specially designed traditional method combination, then a Deep Regression Neural Network (DRNN) is adopted to predict the orientations fields, showing much faster convergence speed during training process than classification networks with the same backbone structure. Novel structure for DNN design is proposed to solve problem of discontinuity around 0° and increase prediction accuracy. Experimental results on test database proves that proposed algorithm system defeats state-of-the-art fingerprint orientation estimation algorithms.