IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint analysis and singular point detection
Pattern Recognition Letters
Fingerprint orientation field estimation using ridge projection
Pattern Recognition
Fingerprint orientation field estimation using ridge projection
Pattern Recognition
Computer Vision and Image Understanding
A novel technique for palmprint classification and authentication
International Journal of Biometrics
Computationally efficient fingerprint algorithm for automatic recognition
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
ATPDI: a computational definition of fingerprint singular points
International Journal of Information Technology and Management
A variational formulation for fingerprint orientation modeling
Pattern Recognition
Improved pre-processing algorithm in spatial scalability for scalable video coding
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Fingerprint orientation field reconstruction by weighted discrete cosine transform
Information Sciences: an International Journal
Hi-index | 0.01 |
A Bayesian formulation is proposed for reliable and robust extraction of the directional field in fingerprint images using a class of spatially smooth priors. The spatial smoothness allows for robust directional field estimation in the presence of moderate noise levels. Parametric template models are suggested as candidate singularity models for singularity detection. The parametric models enable joint extraction of the directional field and the singularities in fingerprint impressions by dynamic updating of feature information. This allows for the detection of singularities that may have previously been missed, as well as better aligning the directional field around detected singularities. A criteria is presented for selecting an optimal block size to reduce the number of spurious singularity detections. The best rates of spurious detection and missed singularities given by the algorithm are 4.9% and 7.1%, respectively, based on the NIST 4 database.