Fingerprint pose estimation based on faster R-CNN

Abstract

Fingerprint pose estimation is one of the bottlenecks of indexing in large scale database. The existing methods of pose estimation are based on manually appointed features (e.g. special points, ridges, orientation filed). In this paper, we propose a method based on deep learning to achieve accurate pose estimation. Faster R-CNN is adopted to detect the center point and rough direction, followed by intra-class and inter-class combination to calculate the precise direction. Extensive experiments on NIST-14 show that (1) the predicted poses are close to manual annotations even when the fingerprints are incomplete or noisy, (2) the estimated poses for matching fingerprint pairs are very consistent and (3) by registering fingerprints using the estimated pose, the accuracy of a state-of-the-art fingerprint indexing system is further improved.