Extracting image orientation feature by using integration operator
Pattern Recognition
Efficient fingerprint search based on database clustering
Pattern Recognition
Fingerprint classification based on Adaboost learning from singularity features
Pattern Recognition
r-Theta and orientation invariant transform and signal combining for fingerprint recognition
Expert Systems with Applications: An International Journal
An improved representation of junctions through asymmetric tensor diffusion
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Invariant representation of orientation fields for fingerprint indexing
Pattern Recognition
Fingerprint orientation field reconstruction by weighted discrete cosine transform
Information Sciences: an International Journal
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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.