Advertisement

Abstract

Indexing is the process of assigning a numerical value to a database entry in order to facilitate its rapid retrieval. Indexing a fingerprint database can reduce the search space and improve the response time of an identification system. We discuss a novel method for generating index codes for fingerprint images by using a small set of pre-determined reference fingerprints. In the proposed method, the match scores generated by comparing an input fingerprint with the reference fingerprints are subjected to a discretization function, which converts them into an index code. A search mechanism based on the Hamming distance identifies those index codes in the database that are similar to the code of the input image. The proposed technique has several advantages: it obviates the need to extract complex features from the fingerprint image; it utilizes the matcher that is already associated with a particular application; and it can be used to index any biometric database irrespective of the trait or matcher being used. Experimental results on two fingerprint databases (NIST-4 and WVU) indicate that the proposed encoding scheme generates index codes that are well-scattered thereby allowing noisy query images to be indexed correctly.

References

  1. 1.
    International Biometric Group, Henry Classification System (2003), http://www.biometricgroup.com/HenryFingerprintClassification.pdf
  2. 2.
    Bebis, G., Deaconu, T., Georgiopoulos, M.: Fingerprint Identification Using Delaunay Triangulation. In: International Conference on Information Intelligence and Systems (ICIIS), pp. 452–459 (1999)Google Scholar
  3. 3.
    Bhanu, B., Tan, X.: Fingerprint Indexing Based on Novel Features of Minutiae Triplets. IEEE Trans. Pattern Analysis and Machine Intelligence 25(5), 616–622 (2003)CrossRefGoogle Scholar
  4. 4.
    Crihalmeanu, S., Ross, A., Schuckers, S., Hornak, L.: A Protocol for Multibiometric Data Acquisition, Storage and Dissemination. Technical Report, WVU, Lane Department of Computer Science and Electrical Engineering (2007)Google Scholar
  5. 5.
    Feng, J., Cai, A.: Fingerprint Indexing Using Ridge Invariants. In: International Conference Pattern Recognition, vol. 4, pp. 433–436 (2006)Google Scholar
  6. 6.
    Germain, R.S., Califano, A., Colville, S.: Fingerprint Matching Using Transformation Parameter Clustering. IEEE Computational Science & Engineering 4(4), 42–49 (1997)CrossRefGoogle Scholar
  7. 7.
    Jain, A., Pankanti, S.: Automated Fingerprint Identification and Imaging Systems. In: Lee, H.C., Gaensslen, R.E. (eds.) Advances in Fingerprint Technology, 2nd edn. CRC Press, Boca Raton (2001)Google Scholar
  8. 8.
    Lin, S., Costello, D.J.: Error Control Coding, 2nd edn. Prentice-Hall, Inc., Upper Saddle River (2004)zbMATHGoogle Scholar
  9. 9.
    Lumini, A., Maio, D., Maltoni, D.: Continuous Versus Exclusive Classification for Fingerprint Retrieval. Pattern Recognition Letters 18(10), 1027–1034 (1997)CrossRefGoogle Scholar
  10. 10.
    Maeda, T., Matsushita, M., Sasakawa, K.: Identification Algorithm Using a Matching Score Matrix. IEICE Trans. Information and Systems, Special Issue on Biometric Person Authentication, The Institute of Electronics, Information and Communication Engineers 84(7), 819–824 (2001)Google Scholar
  11. 11.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)zbMATHGoogle Scholar
  12. 12.
    Mhatre, A., Palla, S., Chikkerur, S., Govindaraju, V.: Efficient Search and Retrieval in Biometric Databases. In: SPIE Biometric Technology for Human Identification II, pp. 265–273 (March 2005)Google Scholar
  13. 13.
    Watson, C.J., Wilson, C.L.: NIST Special Database 4, Fingerprint database: Users’ Guide. U.S. National Institute for Standards and Technology (1992), http://www.nist.gov/data/WebGuide/SD_4/FingerprintDB_4.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Aglika Gyaourova
    • 1
  • Arun Ross
    • 1
  1. 1.West Virginia UniversityMorgantownUSA

Personalised recommendations