Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Effective Morphological Extraction of True Fingerprint Minutiae based on the Hit or Miss Transform
Roli Bansal, Priti Sehgal, Punam Bedi
Pages - 71 - 85     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
minutiae extraction, Hit or Miss transform, Biometrics, fingerprint image, mathematical morphology
Fingerprints are the most widely used parameter for personal identification amongst all biometrics based personal authentication systems. As most Automatic Fingerprint Recognition Systems are based on local ridge features known as minutiae, marking minutiae accurately and rejecting false ones is critically important. In this paper we propose an algorithm for extracting minutiae from a fingerprint image using the binary Hit or Miss transform (HMT) of mathematical morphology. We have developed and tested structuring elements for different types of minutiae present in a fingerprint image to be used by the HMT after preprocessing the image with morphological operators. This results in efficient minutiae detection, thereby saving a lot of effort in the post processing stage. The algorithm is tested on a large number of images. Experimental results depict the effectiveness of the proposed technique.
CITED BY (26)  
1 Pushparaj, V., Arumugam, B., & Gurunathan, U. (2015). A variant approach for human forensic identification using dental radiographs with skeleton and contour. International Journal of Signal and Imaging Systems Engineering, 8(1-2), 59-67.
2 Karthikeyini, C., & Rajamani, V. (2015). Study on Fingerprint Images Using Delaunay Patterns to Identify Hereditary Relations Among Family Members of Three Generations. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (pp. 431-440). Springer India.
3 Tiwari, K., Tiwari, G., & Gupta, P. (2015, January). Extraction of high confidence minutiae points from fingerprint images. In Computer and Computational Sciences (ICCCS), 2015 International Conference on (pp. 238-243). IEEE.
4 Kaushik, V. D., & Gupta, P. (2015, September). An Efficient Fingerprint Minutiae Detection Algorithm. In Security in Computing and Communications: Third International Symposium, SSCC 2015, Kochi, India, August 10-13, 2015. Proceedings (Vol. 536, p. 186). Springer.
5 Karthikeyini, C., Rajamani, V., & Bommanna Raja, K. (2014). Exploring fingerprints using composite minutiae descriptors to determine hereditary relation across multiple generations. International Journal of Biomedical Engineering and Technology, 15(3), 224-242.
6 Pushparaj, V., Gurunathan, U., & Arumugam, B. (2014). Victim identification with dental images using texture and morphological operations. Journal of Electronic Imaging, 23(1), 013004-013004.
7 Geethanjali, N. New level of Security in ATM System Using Brain Fingerprinting.
8 Bannsal, R. (2014). Computationally intelligent watermarking for securing fingerprint images.
9 Verma, R., & Kaur, R. (2014). An efficient technique for character recognition using neural network and SURF feature extraction. Int. J. Comput. Sci. Inf. Technol, 5, 1995-1997.
10 Lawand, S. J., & Chatterjee, M. (2013). Secure Cryptosystem with Blind Authentication. In Computer Networks & Communications (NetCom) (pp. 489-498). Springer New York.
11 Chockaian, K., Vayanaperumal, R., & Kanagaraj, B. R. (2013). New approach for identifying hereditary relation using primary fingerprint patterns. Image Processing, IET, 7(5), 423-431.
12 Reddy, Y. P. (2013). An efficient Fingerprint Minutiae Extraction Algorithm (Doctoral dissertation, indian institute of technology kanpur).
13 Pushparaj, V., Gurunathan, U., & Arumugam, B. (2013). Missing tooth identification and teeth numbering in dental X-ray and photographic imaging. International Journal of Biomedical Engineering and Technology, 13(2), 185-200.
14 Geethanjali, N., & Thamaraiselvi, K. (2013). Feature Level Fusion of Multimodal Biometrics and Two Tier Security in ATM System. International Journal of Computer Applications, 70(14), 17-23.
15 Meena, V. (2013). Fingerprint Recognition System Using Multi-algorithm Score Level Fusion (Doctoral dissertation, indian institute of technology kanpur).
16 Geethanjali, N., & Thamaraiselvi, K. Enhancing the Security of Biometrics in ATM.
17 Verma, R., & Kaur, M. R. Enhanced Character Recognition Using Surf Feature and Neural Network Technique.
18 Bansal, R., Sehgal, P., & Bedi, P. (2012). Securing Fingerprint Images Through PSO Based Robust Facial Watermarking. International Journal of Information Security and Privacy (IJISP), 6(2), 34-52.
19 Leng, W. Y., & Shamsuddin, S. M. (2012). Fingerprint identification using discretization technique. Engineering and Technology, 62, 709-717.
20 Awasthi, V., Awasthi, V., & Tiwari, K. K. (2012). Fingerprint analysis using termination And bifurcation minutiae. International Journal of Emerging Technology and Advanced Engineering, 2, 124-30.
21 Bansal, R., Sehgal, P., & Bedi, P. (2012). Securing Fingerprint Images Using PSO-Based Wavelet Domain Watermarking. Information Security Journal: A Global Perspective, 21(2), 88-101.
22 DI, T. D. D. D. R. Contactless Fingerprint Biometrics: Acquisition, Processing, and Privacy Protection.
23 Leng, W. Y., & Shamsuddin, S. M. Fingerprint Identification using Discretization.
24 Jubair, M. I., & Banik, P. (2012). A simplified method for handwritten character recognition from document image. International Journal of Computer Applications, 51(14).
25 Bansal, R., Sehgal, P., & Bedi, P. (2011). Minutiae extraction from fingerprint images-a review. arXiv preprint arXiv:1201.1422.
26 R. Bansal, P. Sehgal and P. Bedi, “Minutiae Extraction from Fingerprint Images - a Review” Publication: International Journal of Computer Science Issues (IJCSI), 8(5), pp. 74-85, September 2011.
1 Directory of Open Access Journals (DOAJ)
2 Google Scholar
3 Academic Journals Database
4 ScientificCommons
5 Academic Index
6 CiteSeerX
7 refSeek
9 Socol@r
10 ResearchGATE
11 Libsearch
12 Bielefeld Academic Search Engine (BASE)
13 Scribd
14 WorldCat
15 slideshare
17 PdfSR
18 Free-Books-Online
1 A. George, “Multi-Modal Biometrics Human Verification Using LDA and DFB”, International Journal of Biometrics and Bioinformatics, vol. 2, issue 4, 2008.
2 A. Jain, L. Hong, “Online Fingerprint Verification”, IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 4 (1997), 302–314.
3 B.G. Sherlock, D. M. Monro, K. Millard, “ Fingerprint Enhancement by Directional Fourier Filtering”, IEE Proc. Image Signal Process, Vol. 141, No. 2, April 1994.
4 C. S. Lee, Y. H. Kuo, “Adaptive fuzzy filter and its application to image enhancement,” in Fuzzy techniques in Image Processing , I edition E. E. Kerre and M. Nachtegael , Eds. , Heidelberg, germany: Physica Verlag, 2000 , vol. 52, pp. 172-193.
5 D. V. Jadhav, V.M.Mane, “Review of Multimodal Biometrics: Applications, Challenges and Research Areas”, International Journal of Biometrics and Bioinformatics, vol. 3, issue 5, 2009.
6 D. Trier, T. Taxt,(1995) “Evaluation of binarisation methods for document images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 3, pp.312–315.
7 D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. Springer, 2003.
8 Feng Zhao, Xiaou Tang, “Preprocessing and post processing for skeleton-based fingerprint minutiae extraction”, Pattern Recognition 40, p.no. 1270-1281, 2007.
9 J. Luping, Y. Zhang , S. Lifeng, P. Xiaorong, “Binary Fingerprint Image Thinning using PCNNs”, IEEE Trans. On Systems, Man and cybernetics, Part B, vol. 7 , No. 5, October 2007.
10 J. C. Amengual, A. Juan, J. C. Prez, Prat, F., Sez, S., and Vilar, J. M. Real-time minutiae extraction in fingerprint images. In Proc. of the 6th Int. Conf. on Image Processing and its Applications (July 1997), pp. 871–875.
11 L.Hong, Y. Wan, A. K. Jain “Fingerprint Image Enhancement: Algorithm and Performance Evaluation” IEEE Transaction on pattern analysis and machine intelligence, Vol 20 No. 8, p.p. 777-789, 1998.
12 M. A. Oliveira, N. J. Leite, “A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images”, Pattern Reognition 41, p.no. 367-377 , 2008.
13 M. Tico, M. Vehvilainen, J. Saarinen, "A method of fingerprint image enhancement based on second directional derivatives" (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. Volume 2, March 18-23, PP:985 – 988, 2005
14 M. Tico and P. Kuosmanen, “ An algorithm for fingerprint image postprocessing”, In Proceedings of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (November 2000), vol. 2, pp. 1735–1739.
15 M. Usman Akram , A. Tariq, Shoaib A. Khan, “Fingerprint image : pre and post processing”, Int. Journal of Biometrics, Vol. 1, No.1, 2008.
16 M. Kaur, M. Singh, P.S. Sandhu, “Fingerprint Verification system using Minutiae Verification Technique”, Proceedings of world Academy of Science, Engineering and Technology, vol. 36, Dec. 2008.
17 N. Otsu, “A threshold selection method from gray level histograms”, IEEE Transactions Analysis and machine intelligence, June 1990.
18 N. Ratha, S. Chen, and A. Jain, Adaptive flow orientation based feature extraction in fingerprint images. Pattern Recognition 28, 11 (1995), 1657–1672.
19 Q. Xiao and H. Raafat, “Fingerprint image postprocessing: a combined statistical and structural approach” Pattern Recognition 24, 10 (1991), 985–992.
20 R. Bansal, P. Sehgal, P. Bedi, “Fingerprint Image Enhancement Using Type-2 fuzzy sets”, IEEE International Conference on Fuzzy Systems and Knowledge Discovery(FSKD’2009), August 2009.
21 R. Bansal, P. Sehgal, P. Bedi, “A novel framework for enhancing images corrupted by impulse noise using type-II fuzzy sets”, IEEE International Conference on Fuzzy Systems and Knowledge Discovery(FSKD’2008), vol. 3, pp 266-271, October 2008.
22 R. C. Gonzalez, R. E. Wood, “Digital Image Processing”, Second Edition, Prentice Hall,2006.
23 S. Greenberg, M. Aladjem, D. Kogan and I. Dimitrov “Fingerprint Image Enhancement using Filtering Techniques” ,Electrical and Computer Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel, 2000.
24 S. Kasaei, M. D., and B. Boashash, Fingerprint feature extraction using block-direction on reconstructed images. In IEEE region TEN Conf., digital signal Processing applications, TENCON (December 1997), pp. 303–306.
25 S.K. Oh, J.J. Lee, C.H. Park, B.S. Kim, K.H. Park, “New Fingerprint Image Enhancement Using Directional Filter Bank”, School of Electrical Engineering, Kyungpook National University SEOUL, Daegu, Korea, School of Internet Engineering, Dongseo University SEOUL , Daegu, Korea.
26 S. Prabhakar, J. Wang, A. K. Jain, S. Pankanti, and Bolle, R. “Minutiae Verification and classification for fingerprint matching”. In Proc. 15th International Conference Pattern Recognition (ICPR) (September 2000), vol. 1, pp. 25–29.
27 S. M Mohsen, S. M. Zamshed, M. M. A. Hashem, “Automated Fingerprint Recognition : Using Minutiae Matching Technique for the large Fingerprint Database”, III International conference on Electrical and Computer Engineering ICECE 2004, 28-30 December, 2004,Dhaka, Bangladesh.
28 S. Shah, P. S. Sastry, “ Fingerprint Classification Using a Feedback –Based Line Detector”, IEEE Trans. On Systems, Man and Cybernetics, Part B, vol. 34, no.1, February 2004.
29 V. Espinosa, “Mathematical Morphological approaches for Fingerprint Thinning”, IEEE, 2002.
30 Chander Kant, R. Nath, “Reducing Process Time for Fingerprint Identification System”, International Journal of Biometrics and Bioinformatics, vol. 3, issue 1, 2009.
31 X. Jiang, W.-Y. Yau, and W. Ser, “Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge”, Pattern Recognition, 34(5):999–1013, 2001.
32 Y.S. Choi and R. Krishnapuram , “ A robust approach to image enhancement based on fuzzy logic”, IEEE Trans. Image Process., vol 6, no. 6, pp 808-825, Jun 1997.
33 M. T. Yildrim, A. Basturk, “ A Detail Preerving type-2 Fuzzy Logic Filter for Impulse Noise Removal from Digital Images”, Fuzzy Systems Conference, FUZZ-IEEEE, 2007.
34 Zenzo, L. Cinque, and S. Levialdi, "Run-Based Algorithms for Binary Image Analysis and Processing," IEEE Trans. PAMI, vol. 18, no. 1, pp. 83-88, January 1996.
Mr. Roli Bansal
Keshav Mahavidyalaya, University of Delhi - India
Associate Professor Priti Sehgal
Keshav Mahavidyalaya, University of Delhi - India
Associate Professor Punam Bedi
University of Delhi - India