Estimating Fingerprint Pose via Dense Voting

Aligning fingerprint images to a unified coordinate system defined by fingerprint pose is beneficial for fast and accurate fingerprint matching. Due to poor ridge quality and partial observations, however, performance of the state-of-the-art fingerprint pose estimation algorithms remains unsatisfactory. In this study, we propose to fuse voting strategy and deep network to estimate fingerprint center and direction. Rather than regressing them directly, we predict dense offset maps and vote for the final estimation. Experimental results on ten fingerprint datasets with over 60K fingerprints show that (1) highly consistent fingerprint pose estimations are obtained across different impressions of the same finger, (2) performance of fingerprint indexing and verification is further improved thanks to more accurate fingerprint pose estimation, and (3) the proposed approach is more robust to sensing technologies (optical, capacitive, inking, and direct imaging) and impression types (rolled, plain, latent, and contactless).

Automated summary

In this study, a fusion of voting strategy and deep network is proposed to estimate fingerprint center and direction. Experimental results show that this approach can achieve consistent fingerprint pose estimations, improve performance of fingerprint indexing and verification, and be robust to different sensing technologies and impression types.