This Article 
 Bibliographic References 
 Add to: 
A Multichannel Approach to Fingerprint Classification
April 1999 (vol. 21 no. 4)
pp. 348-359

Abstract—Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4,000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.

[1] Advances in Fingerprint Technology, H.C. Lee and R.E. Gaensslen, eds. New York: Elsevier, 1991.
[2] A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System Using Fingerprints," Proc. IEEE, vol. 85, no. 9, pp. 1,365-1,388, 1997.
[3] K. Karu and A.K. Jain, "Fingerprint Classification," Pattern Recognition, vol. 29, no. 3, pp. 389-404, 1996.
[4] L. Hong and A.K. Jain, "Classification of Fingerprint Images," Technical Report MSUCPS:TR98-18, Michigan State Univ., June 1998.
[5] C.I. Watson and C.L. Wilson, "NIST Special Database 4, Fingerprint Database," Nat'l Inst. of Standards and Tech nology, Mar. 1992.
[6] C.L. Wilson, G.T. Candela, and C.I. Watson, "Neural Network Fingerprint Classification," J. Artificial Neural Networks, vol. 1, no. 2, pp. 203-228, 1993.
[7] G.T. Candela, P.J. Grother, C.I. Watson, R.A. Wilkinson, and C.L. Wilson, "PCASYS—A Pattern-Level Classification Automation System for Fingerprints," Technical Report NISTIR 5647, Apr. 1995.
[8] A. Senior, "A Hidden Markov Model Fingerprint Classifier," Proc. 31st Asilomar Conf. Signals, Systems and Computers, pp. 306-310, 1997.
[9] M.M.S. Chong, T.H. Ngee, L. Jun, and R.K.L. Gay, "Geometric Framework for Fingerprint Classification," Pattern Recognition, vol. 30, no. 9, pp. 1,475-1,488, 1997.
[10] C.V.K. Rao and K. Black, "Type Classification of Fingerprints: A Syntactic Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, no. 3, pp. 223-231, 1980.
[11] A.P. Fitz and R.J. Green, "Fingerprint Classification Using Hexagonal Fast Fourier Transform," Pattern Recognition, vol. 29, no. 10, pp. 1,587-1,597, 1996.
[12] B.G. Sherlock and D.M. Monro, "A Model for Interpreting Fingerprint Topology," Pattern Recognition, vol. 26, no. 7, pp. 1,047-1,055, 1993.
[13] M. Kawagoe and A. Tojo, "Fingerprint Pattern Classification," Pattern Recognition, vol. 17, no. 3, pp. 295-303, 1984.
[14] A.K. Jain, L. Hong, and R. Bolle, On-Line Fingerprint Verification IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 302-314, Apr. 1997.
[15] J.G. Daugman, "Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles," Vision Research, vol. 20, pp. 847-856, 1980.
[16] J.G. Daugman,“High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1,148-1,161, Nov. 1993.
[17] A.K. Jain and F. Farrokhnia, “Unsupervised Texture Segmentation Using Gabor Filters,” Pattern Recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[18] J.G. Daugman, "Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters," J. Optical Soc. Amer. A, vol. 2, pp. 1,160-1,169, 1985.
[19] A.K. Jain and B. Chandrasekaran, "Dimensionality and Sample Size Considerations in Pattern Recognition Practice," Handbook of Statistics, vol. 2, P.R. Krishnaiah and L.N. Kanal, eds., pp. 835-855. North-Holland, 1982,
[20] N. Ratha, S. Chen, and A.K. Jain, "Adaptive Flow Orientation-Based Feature Extraction in Fingerprint Images," Pattern Recognition, vol. 28, no. 11, pp. 1,657-1,672, 1995.
[21] N. Ratha, K. Karu, S. Chen, and A.K. Jain, "A Real-Time Matching System for Large Fingerprint Databases," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 799-813, Aug. 1996.
[22] S.E. Fahlman, "Faster-Learning Variations on Back-Propagation: An Empirical Study," Proc. 1988 Connectionist Models Summer School, 1988.
[23] R. Duda, P. Hart, and D. Stork, Pattern Classification. New York: John Wiley&Sons, 2001.
[24] J.L. Wayman, "Technical Testing and Evaluation of Biometric Identification Devices," Biometrics: Personal Identification in Networked Society, A.K. Jain, R. Bolle, and S. Pankanti, eds. Kluwer, 1998.
[25] K. Woods, W.P. Kegelmeyer, and K.W. Bowyer, "Combination of Multiple Classifiers Using Local Accuracy Estimates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 405-410, Apr. 1997.

Index Terms:
Biometrics, fingerprint classification, Gabor filters, neural networks, FingerCode.
Anil K. Jain, Salil Prabhakar, Lin Hong, "A Multichannel Approach to Fingerprint Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 348-359, April 1999, doi:10.1109/34.761265
Usage of this product signifies your acceptance of the Terms of Use.