Humanoid Fingerprint Recognition based on Fuzzy Neural Network

Nowadays the computer speed is much faster than before, however well-trained humans are still the best pattern recognizer. In this paper we propose a fingerprint recognition method which is based on humanoid algorithms. Because fingerprint patterns are fuzzy in nature and ridge endings are changed easily by scars, we try to only use ridge bifurcation as fingerprints minutiae and also design a "fuzzy feature image" encoder by using cone membership function to represent the structure of ridge bifurcation features extracted from fingerprint. Then, we integrate the fuzzy encoder with back-propagation neural network (BPNN) as a recognizer which has variable fault tolerances for fingerprint recognition. Experimental results show that the humanoid fingerprint recognition system is robust, reliable and rapid.

[1]  L. L. Hoberock,et al.  Machine vision fuzzy object recognition and inspection using a new fuzzy neural network , 1996, Proceedings of the 1996 IEEE International Symposium on Intelligent Control.

[2]  Ming Lu.  Improved neural network modeling approach for engineering applications , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[3]  Xudong Jiang,et al.  Effective and efficient fingerprint image postprocessing , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[4]  Henry O. Nyongesa,et al.  Automatic fingerprint classification system using fuzzy neural techniques , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[5]  Sharath Pankanti,et al.  FingerCode: a filterbank for fingerprint representation and matching , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[6]  M. Ballan.  Directional fingerprint processing , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).

[7]  Shashi Shekhar,et al.  Customizing parallel formulations of backpropagation learning algorithm to neural network architectures: a summary of result , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[8]  Mohammad-Hassan Ghassemian.  A robust on-line restoration algorithm for fingerprint segmentation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[9]  M. Er,et al.  Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems , 2003, IEEE Trans. Fuzzy Syst..

[10]  V. K. Sagar,et al.  Fuzzy feature selection for fingerprint identification , 1995, Proceedings The Institute of Electrical and Electronics Engineers. 29th Annual 1995 International Carnahan Conference on Security Technology.

[11]  I. Garcia-Moral,et al.  Control architecture based on a radial basis function network. Application to a fluid level system , 1996, Proceedings of International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing.

[12]  Halit Eren,et al.  Modular artificial neural network for prediction of petrophysical properties from well log data , 1996, Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Tec.

[13]  Minoru Inamura,et al.  Spatial resolution improvement of remotely sensed images by a fully interconnected neural network approach , 2000, IEEE Trans. Geosci. Remote. Sens..

[14]  Sen Wang,et al.  Fingerprint enhancement in the singular point area , 2004, IEEE Signal Processing Letters.

[15]  Barry W. Johnson,et al.  Modeling of fault tolerance in neural networks , 1989, 15th Annual Conference of IEEE Industrial Electronics Society.

[16]  Sharath Pankanti,et al.  Minutia verification and classification for fingerprint matching , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.