Fingerprint classification plays an important role in automatic recognition of fingerprints from a given dataset. It significantly reduces the time taken to map a fingerprint to its nearest match by providing a broad classification of given fingerprint into its relevant class and performing the further search in that class domain only. Various rule-based, model-based and structure-based approaches have been proposed and used to perform such classification. This paper discusses the various mechanism employed to categorize fingerprints into basic classes like arch, whorl, left loop, right loop and tented arch along with the advantages and limitations of each approach. The paper aims to provide a concise study and performance based comparison of various fingerprint classification approaches and the different techniques they use to perform the classification.

Fingerprint Classification, statistical classifiers, rule-based classifiers, neural networks, structural classifiers, hybrid classifiers

[1] Cole, Simon A., “Suspect Identities: A History of Fingerprint and Criminal Identification”, Harvard University Press, London, 1967.
[2] Kücken,M., Newell, AC , “Fingerprint formation”, Journal of theoretical biology, Elsevier,2005.
[3] Germain, R., Califano, A., Colville, S.,"Fingerprint matching using transformation parameter clustering", IEEE Computational Science and Engineering, Vol. 4, No. 4, pp. 42–49, 1997.
[4] Jain, A.K., Prabhakar, S., Hong, L.,"A Multichannel Approach to Fingerprint Classification", Proc. of Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP’98), New Delhi, India, 1998.
[5] Feng, J., & Jain, A. K., “Fingerprint reconstruction: from minutiae to phase”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2), 209-223,2011
[6] E.R. Henry, Classification and Uses of Finger Prints, Routledge, London, 1900.
[7] Karu, K., Jain, A.K.,"Fingerprint Classification”, Proceedings of Pattern Recognition, Vol. 29, No. 3, pp.389-404, 1996.
[8] Ballan. M., Sakarya, F.A., Evans, B.L.,"Directional Fingerprint Processing", Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA , USA Vol. 2, pp. 101-104, 1997
[9] Cho, B.H., Kim, J.S., Bae, J.H., Bae, I.G., Yoo, K.Y., Byoung-Ho, C., Jeung-Seop, K., Jae-Hyung, B., In-Gu, B., Kee-Young, Y.,"Fingerprint Image Classification by Core Analysis", 5th International Conference on Signal Processing Proceedings, WCCC-ICSP 2000, Vol. 3, pp. 1534 – 1537, 2000.
[10] Wei, L.,"Fingerprint Classification using Singularities Detection", International Journal of Mathematics and Computers in Simulation, Vol. 2, Issue 2, pp. 158-162, 2008.
[11] Suralkar, S., Rane, M.E., Patil, P.M.,"Fingerprint Classification Based on Maximum Variation in Local
Orientation Field", International Journal of Computing Science and Communication Technologies, Vol. 2, No. 1, pp. 277-280, 2009.
[12] Leandra Webb and Mmamolatelo Mathekga, “Towards A Complete Rule-Based Classification Approach for Flat Fingerprints,” 2014 IEEE Second International Symposium On Computing And Networking, 978-1-4799-4152-0/14, pp. 549-556, 2014.
[13] RaffaeleCappelli, Alessandra Lumini, Dario Maio, and DavideMaltoni, “Fingerprint Classification By Directional Image Partitioning,” IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 21, No. 5, 402-422, pp.402-424, May 1999.
[14] Nain, N., Bhadviya, B., Gautam, B., Kumar, D., Deepak, B.M.,"A Fast Fingerprint Classification Algorithm by Tracing Ridge–flow Patterns", IEEE International Conference on Signal Image Technology and Internet Based Systems, IEEE Computer Society, pp. 235-238.
[15] Wei, L., Yonghui,, C., Fang, W.,"Fingerprint Classification by Ridgeline and Singular Point Analysis", Congress on Image and Signal Processing, IEEE Computer Society, pp. 594-598, 2008.
[16] Tarjom an, M., Zarei, S.,"Automatic Fingerprint Classification using Graph Theory", Proceedings of World Academy of Science, Engineering and Technology, Vol. 47, pp. 214- 218, 2008.
[17] Ji, L., Yi, Z.,"SVM-based Fingerprint Classification Using Orientation Field", 3rd International conference on Natural Computation, Vol. 2, pp. 724-727, 2007.
[18] Li, J., Yau, W.Y., Wang, H.,"Combining singular points and orientation image information for fingerprint classification", Pattern Recognition, Vol. 41, Issue 1, pp. 353-366, 2007.
[19] Xuanbin Si, JianjiangFeng, Jie Zhou and YuxuanLuo, “Detection and Rectificationof Distorted Fingerprints,” IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 37, NO.3, pp. 555-569, March2015.
[20] Wang, S., Zhang, W.W. Wang, Y.S.,"Fingerprint Classification by Directional Fields", Proceedings of the Fourth IEEE International Conference on Multimodal Interfaces (ICMI’02), IEEE Computer Society, pp. 2002.
[21] Tan, X., Bhanu, B., Lin, Y.,"Learning Features for Fingerprint Classification", AVBPA 2003, LNCS- 2688, pp. 318-326, 2003.
[22] K. C. Leung and C. H. Leung, “Improvement Of Fingerprint Retrieval By A Statistical Classifier,” IEEE Transactions On Information Forensics And Security, Vol. 6, No. 1, pp. 59-70. March 2011.
[23] Kamijo, M.,"Classifying Fingerprint Images using Neural Network: Deriving the Classification State", IEEE International Conference on Neural network, Vol. 3, pp. 1932-1937, 1993.
[24] Mohamed, S. M., Nyongesa, H.,"Automatic Fingerprint Classification System using Fuzzy Neural techniques", IEEE International Conference on Artificial Neural Networks, Vol. 1, pp. 358-362, 2002.
[25] Yao, Y., Marcialis, G.L., Pontil, M.,"A new machine learning approach to fingerprint classification", 7th Congress of the Italian Association for Artificial Intelligence, pp. 57-63, 2001.
[26] Shah, S., Sastry P.S.,"Fingerprint Classification Using a Feedback-Based Line Detector", IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 34, No. 1, pp. 85- 94, 2004.
[27] Park, C., Park, H.,"Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis", Pattern Recognition, Vol. 38, No. 4, pp. 495-503, 2008.