Pore-based ridge reconstruction for fingerprint recognition

The use of sweat pores in fingerprint recognition is becoming increasingly popular, mostly because of the wide availability of pores, which provides complementary information for matching distorted or incomplete images. In this work we present a fully automatic pore-based fingerprint recognition framework that combines both pores and ridges to measure the similarity of two images. To obtain the ridge structure, we propose a novel pore-based ridge reconstruction approach by considering a connect-the-dots strategy. To this end, Kruskal's minimum spanning tree algorithm is employed to connect consecutive pores and form a graph representing the ridge skeleton. We evaluate our framework on the PolyU HRF database, and the obtained results are favorably compared to previous results in the literature.

[1]  Keith E. Muller,et al.  Contrast-limited adaptive histogram equalization: speed and effectiveness , 1990, [1990] Proceedings of the First Conference on Visualization in Biomedical Computing.

[2]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[3]  Carlotta Domeniconi,et al.  Direct gray scale ridge reconstruction in fingerprint images , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[4]  David Zhang,et al.  Fingerprint Pore Matching Based on Sparse Representation , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Venu Govindaraju,et al.  Fingerprint enhancement using STFT analysis , 2007, Pattern Recognit..

[6]  Yaxuan Qi.  Fingerprint Ridge Line Reconstruction , 2004, Intelligent Information Processing.

[7]  Yi Chen,et al.  Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features , 2007 .

[8]  Maurício Pamplona Segundo,et al.  Dynamic Pore Filtering for Keypoint Detection Applied to Newborn Authentication , 2014, 2014 22nd International Conference on Pattern Recognition.

[9]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  David Zhang,et al.  Direct Pore Matching for Fingerprint Recognition , 2009, ICB.

[11]  J. Kim,et al.  Fingerprint Matching Incorporating Ridge Features With Minutiae , 2011, IEEE Transactions on Information Forensics and Security.

[12]  David Zhang,et al.  Adaptive fingerprint pore modeling and extraction , 2010, Pattern Recognit..

[13]  S. Malathi,et al.  Improved Partial Fingerprint Matching Based on Score Level Fusion Using Pore and SIFT Features , 2011, 2011 International Conference on Process Automation, Control and Computing.

[14]  Neucimar Jerônimo Leite,et al.  Improving pore extraction in high resolution fingerprint images using spatial analysis , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[15]  Anil K. Jain,et al.  Compression of fingerprint images using hybrid image model , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[16]  David Zhang,et al.  High resolution partial fingerprint alignment using pore-valley descriptors , 2010, Pattern Recognit..