A robust fingerprint recognition algorithm should tolerate the rotation and translation of the fingerprint image. One popular solution is to consistently detect a unique reference point and compute a unique reference orientation for translational and rotational alignment. This paper develops an effective algorithm to locate a reference point and compute the corresponding reference orientation consistently and accurately for all types of fingerprints. To compute the reliable orientation field, an improved orientation smoothing method is proposed based on adaptive neighborhood. It shows better performance in filtering noise while maintaining the orientation localization than the conventional averaging method. The reference-point localization is based on multiscale analysis of the orientation consistency to search the local minimum. The unique reference orientation is computed based on the analysis of the orientation differences between the radial directions from the reference point, which are the directions of the radii emitted from the reference point with equivalent angle interval, and the local ridge orientations along these radii. Experimental results demonstrate that our proposed algorithm can consistently locate a unique reference point and compute the reference orientation with high accuracy for all types of fingerprints.

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multiscale analysis of the orientation consistency translational and rotational alignment robust fingerprint recognition algorithm filtering noise averaging adaptive neighborhood fingerprints rotation and translation of the fingerprint unique reference orientation orientation smoothing method referencepoint localization local ridge orientations

Alex Chichung Kot,Xudong Jiang,Manhua Liu,.Fingerprint Reference-Point Detection. 2005 (4),.

Alex Chichung Kot,Xudong Jiang,Manhua Liu,"Fingerprint Reference-Point Detection" 2005

Alex Chichung Kot,Xudong Jiang,Manhua Liu,"Fingerprint Reference-Point Detection"