A Morphological Fingerprint Minutiae Annotation Algorithm for Deep Learning Datasets

Minutiae extraction is vital for automatic finger-print identification system. Fingerprint datasets (databases) for minutiae extraction are beneficial to improve the performance of subsequent indexing and matching tasks, particularly combined with advanced deep learning technique. However, the learning-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To thoroughly address these issues (composed of lacking of the available databases and general annotation technique), a novel fingerprint image labeling approach is first focused on in this paper. Specifically, we devise a knowledge-oriented minutiae annotation method, which consists of image segmentation, normalization, orientation and frequency estimations, enhancement, binarization, thining, minutiae extraction, deleting redundancy and then revising the wrong annotations manually. Next, a minutiae database is created via the approach, and then experiments verify its effectiveness for training deep learning model. Comprehensive experimental results on two datasets manifest the ability of the constructed database to improve the prediction accuracy of CNN (achieving precision and recall up to 89.97% and 88.38%, respectively) compared with public software and other similar deep learning extraction model which in turn reveals the effectiveness of our method.