On orientation and anisotropy estimation for online fingerprint authentication

Local dominant orientation estimation is one of the most important operations in almost all automatic fingerprint authentication systems. Robust orientation and anisotropy estimation improves the system's reliability in handling low-quality fingerprints, which is crucial for the system's massive application such as securing multimedia. This paper analyzes the robustness of the orientation and anisotropy estimation methods, and the effect of the modulus normalization on the estimation performance. A two-stage averaging framework with block-wise modulus handling is introduced to inherit the merits of the both linear and normalized averaging methods. We further propose to set the modulus of an orientation vector to be its anisotropy estimate instead of unity so that the orientation inconsistency of gradients is included in the second stage of averaging. These two measures improve the robustness of the fingerprint local dominant orientation estimation and lead to an anisotropy estimate that reflects the characteristics of fingerprint more effectively. In addition, the proposed approach is computationally efficient for online fingerprint authentication. Extensive experiments using both synthetic images and real fingerprints verify the feasibility of the proposed approach and demonstrate its robustness to noise and low-quality fingerprints.