A Novel Application of the Classifier DECOC Based on Fingerprint Identification

Human fingerprints are rich in details, here called "minutiae". In this paper, a fingerprint recognition system based on a novel application of the classifier DECOC to the minutiae extraction and on an optimised matching algorithm will be presented. The minutiae extraction has been performed from fingerprint skeletons. To identify the different shapes and types of minutiae, a Data-driven Error Correcting Output Coding (DECOC) has been adopted to work as a classifier. The proposed classifier has been applied throughout the fingerprint skeleton to locate various minutiae. Extracted minutiae have been used then as identification marks for an automatic fingerprint matching that is based on distance, direction and type between two minutiae.

[1]  Atif Bin Mansoor,et al.  A Novel Contourlet Based Online Fingerprint Identification , 2009, COST 2101/2102 Conference.

[2]  Ming Lu,et al.  Proceedings of the Third International Conference on Machine Learning and Cybernetics , 2004 .

[3]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[4]  Alessandro Farina,et al.  Fingerprint minutiae extraction from skeletonized binary images , 1999, Pattern Recognit..

[5]  Dario Maio,et al.  Direct Gray-Scale Minutiae Detection In Fingerprints , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Xudong Jiang,et al.  Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge , 2001, Pattern Recognit..

[7]  Sidan Du,et al.  A Fingerprint Matching Algorithm of Minutia Based on Local Characteristic , 2008, 2008 Fourth International Conference on Natural Computation.

[8]  Pradeep M. Patil,et al.  Rotation invariant thinning algorithm to detect ridge bifurcations for fingerprint identification , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[9]  Ching Y. Suen,et al.  Data-driven decomposition for multi-class classification , 2008, Pattern Recognit..

[10]  B. Sherlock,et al.  Fingerprint enhancement by directional Fourier filtering , 1994 .

[11]  David Zhang,et al.  Adaptive pore model for fingerprint pore extraction , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Sung-Il Chien,et al.  Run Representation Based Minutiae Extraction in Fingerprint Image , 2002, MVA.

[13]  Anil K. Jain,et al.  Adaptive flow orientation-based feature extraction in fingerprint images , 1995, Pattern Recognit..

[14]  Hao Guo.  A hidden Markov model fingerprint matching approach , 2005, 2005 International Conference on Machine Learning and Cybernetics.