Efficient Rectification of Distorted Fingerprints

Recently, distortion rectification based on a single fingerprint image has been shown to be able to significantly improve the recognition rate of distorted fingerprints. However, the computational complexity of such a method is too high to be useful in practice. In this paper, we propose a novel method for the rectification of distorted fingerprints, whose speed is over 30 times faster than the existing method. This significant speedup is due to a Hough-forest-based two-step fingerprint pose estimation algorithm and a support vector regressor-based fingerprint distortion field estimation algorithm. Experimental results on public domain databases show that our method can achieve as good rectification performance as the existing method but meanwhile is significantly faster.

[1]  Davide Maltoni,et al.  Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Nalini K. Ratha,et al.  Effect of controlled image acquisition on fingerprint matching , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[3]  Anil K. Jain,et al.  Performance evaluation of fingerprint verification systems , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Chitra Dorai,et al.  Dynamic behavior analysis in compressed fingerprint videos , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Ruud M. Bolle.  Improved Fingerprint Matching by Distortion Removal , 2001 .

[6]  Zsolt Miklós Kovács-Vajna,et al.  A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, CVPR.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Anil K. Jain,et al.  A Real-Time Matching System for Large Fingerprint Databases , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Sabih H. Gerez,et al.  Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Xinjian Chen,et al.  A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure , 2006, IEEE Trans. Image Process..

[12]  Jianjiang Feng,et al.  Detection and Rectification of Distorted Fingerprints , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Vladimir N. Dvornychenko,et al.  Summary of NIST latent fingerprint testing workshop , 2006 .

[14]  Jie Zhou,et al.  Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints. , 2014, IEEE transactions on pattern analysis and machine intelligence.

[15]  Pillai Praveen Thulasidharan,et al.  Detection and rectification of distorted fingerprints , 2017, 2017 International Conference on Intelligent Computing and Control (I2C2).

[16]  Anil K. Jain,et al.  Fingerprint Image Enhancement: Algorithm and Performance Evaluation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide , 2010 .

[18]  Laurent D. Cohen,et al.  Fingerprint image matching by minimization of a thin-plate energy using a two-step algorithm with auxiliary variables , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

[19]  Anni Cai,et al.  Fingerprint matching using ridges , 2006, Pattern Recognit..

[20]  Lawrence M Wein,et al.  Using fingerprint image quality to improve the identification performance of the U.S. Visitor and Immigrant Status Indicator Technology Program. , 2005, Proceedings of the National Academy of Sciences of the United States of America.