Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks
Eugène C. Ezin
Unité de Recherche en Informatique et Sciences Appliquées Institut de Mathématiques et de Sciences Physiques Université d’Abomey-Calavi, Benin
Feature extraction plays a primary role in pattern recognition classification. Many context-based and problem-based algorithms have been proposed providing good performance in high-quality fingerprint imaging but fail when declining with poor-quality fingerprints. The pyramidal algorithm we present in this paper operates on an image matrix layer for extracting features from ink-and-paper fingerprints. The effectiveness of the pyramidal algorithm compared to the consolidation algorithm is demonstrated using a backpropagation neural network experiment to test preprocessed data.
-  S. Prabhakar, “Fingerprint Classification and Matching Using a Filterbank,” Ph.D. dissertation in Computer Science and Engineering, Michigan State University, USA, 2001.
-  M. M. A. Allah, “Artificial Neural Networks Based Fingerprint Authentification with Cluster Algorithm,” in J. of Informatica 29, pp. 303-307, 2005.
-  R. Thai, “Fingerprint Image Enhancement and Minutiae Extraction,” Dissertation of the school of Computer Science and Software Engineering, University of Western Australia, 2003.
-  V. Espinosa-Duro, “Minutiae Detection Algorithm for Fingerprint Recognition,” in Aerospace and Electronic Systems Magazine, IEEE, pp. 7-10, Vol.17, Issue 3, ISSN 0885-8985, march 2002.
-  A. Jain et al., “An identity Authentication System using Fingerprints,” in Proc. of the IEEE, Vol.85, pp. 1365-1388, 1997.
-  M.C. Bishop, “Neural Networks for Pattern Recognition,” Oxford University Press, Inc., New York, 1995.
-  W. Hill, “Fingerprint recognition,” a Thesis submitted in partial fullfillment of the requirements for the degree of Bachelor of Science in Computer Science,” Hong Kong Baptist University, 2002.
-  M. Antowiak et al., “Fingerprint Identification by Using Artificial Neural Network with Optical Wavelet Preprocessing,” in the Opto-Electronics Review 11(4), pp. 327-337, 2003.
-  Y. Hao et al., “An Effective Algorithm for Fingerprint Matching,” at the URL http://nlpr-web.ia.ac.cn/english/irds/papers/haoying/TENCON.pdf
-  R.M. Bolle et al., “Fingerprint Minutiae: A Constructive Definition,” in the Proc. of Biometric Authentication, pp. 58-62, 2002.
-  M. Poulos, et al., “Fingerprint Verification Based on Image Processing Segmentation Using the Onion Algorithm of Computational Geometry,” in the Proc. of the Sixth Int. Conf. on Mathematics Methods in Scattering Theory and Biomedical Technology (BIOTECH’6) Tsepelovo-Ioannina, Word Scientific, 2003.
-  S. Greenberg et al., “Fingerprint Image Enhancement Using Filtering Techniques,” in the Proc. of Real-Time Imaging, Vol.8, No.3, pp. 227-236(10), 2002.
-  L. Hong, Y. Wan, and A.K. Jain, “Fingerprint Image Enhancement: Algorithms and Performance Evaluation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.20, No.8, pp. 777-789, 1998.
-  J. C. Wu, “Studies of Operational Measurement of ROC Curve on Large Fingerprint Data Sets Using Two-Sample Bootstrap,” Int. Report 7449 of National Institute of Standards and Technology, NIST, Gaithersburg, 2007.
-  Mathworks Inc, “MATLAB Image Processing Toolbox,” User Guide, version 3, 1999-2001.
-  FBI Federal Bureau Investigation, “Inked Fingerprinting Techniques,” at the URL http://www.kyjailers.com/Userfiles/Inked_fingerprint_techniques.pdf
-  C.R. Gonzales, and E.R.Woods, “Digital Image Processing,” Addison Wesley Publishing Company Inc., 1992.
-  G. Cybenko, “Approximation by Superposition of a Sigmoidal Function,” in Mathematics of Control, Signal and Systems, Vol.2, pp. 303-314, 1989.
-  M. Riedlmiller and H. Braun, “A Direct Adaptive Method for Faster Backpropagation Learning: the RPRO algorithm,” in the Proc. of the IEEE Int. Conf. on Neural Networks, 1993.
-  C.E Ezin and A. Avossa, “Fingerprint Identification with Supervised Neural Networks,” in the Proc. of the Int. Conf. on Artificial Neural Networks and Artificial Intelligence, ICANNAI-2003, Minsk, Belarus, pp. 56-60, 2003.
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