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Artificial Intelligence and Evolutionary Algorithms in Engineering Systems pp 695–703Cite as

Improving the Accuracy of Latent Fingerprint Matching Using Texture Descriptors

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 325))

Abstract

Fingerprint matching is a process used to check whether two sets of fingerprint come from the same finger of a person. There are three types of fingerprints in law enforcement applications such as rolled, plain, and latent. Latent fingerprints are partial fingerprint, obtained from the surfaces of objects where a person has touched. It may or may not be an accidental touch. Latent fingerprint contains small area of prints as compared to full fingerprints. We cannot apply a full fingerprint matching algorithm for the latent fingerprint matching. Matching between a latent and a rolled print is a complex task because the number of minutia points will be less. Enhancement of fingerprint is necessary due to low quality of latents and sensor noise. We have done latent fingerprint matching using Hough transform algorithm. Experimental results on NIST latent fingerprint database show an accuracy of 54.43 %. We have enhanced the accuracy by incorporating texture-based features like entropy, correlation, contrast, homogeneity, and energy.

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References

  1. L. Hong, Y. Wan, A. Jain, Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans 20 (1998)

    Google Scholar 

  2. Loannis, Digital image processing algorithm and application (Wiley, New York, 2000). ISBN 0471377392

    Google Scholar 

  3. W. Sen, W. Yangsheng, Fingerprint enhancement in the singular point area. IEEE Signal Process. Lett. 11(1) (2004)

    Google Scholar 

  4. J. Feng, J. Zhou, A performance evaluation of fingerprint minutia descriptors, in Proceedings of International Conference on Hand-Based Biometrics (2011)

    Google Scholar 

  5. X. Jiang, M. Liu, A.C. Kot, Fingerprint retrieval for identification. IEEE Trans. Inf. Forensics Secur. 1(4), 532–542 (2006)

    Google Scholar 

  6. H. Hasan, S. Abdul-Kareem, Fingerprint image enhancement and recognition algorithms: a survey. Springer Neural Comput. Appl. 23(6) (2013)

    Google Scholar 

  7. T. Kiertscher, R. Fischer, C. Vielhauer, Latent fingerprint detection using a spectral texture feature. ACM multimedia workshop on Multimedia and security (2011)

    Google Scholar 

  8. A.M. Bazen, G.T.B. Verwaaijen, S.H. GerezLeo, P.J. Veelenturf, B.J. van der Zwaag, A correlation-based fingerprint verification system, in ProRISC 2000 Workshop on Circuits (2000)

    Google Scholar 

  9. G. Aggarwal, N.K. Ratha, T.Y. Jea, R.M. Bolle, Gradient based textural characterization of fingerprints, in IEEE International Conference on Biometrics (2008)

    Google Scholar 

  10. S.M. Rajbhoj, P.B. Mane, A novel and efficient algorithm of textural feature extraction for fingerprint identification. Int. J. Eng. Res.Technol. 1(5) (2012)

    Google Scholar 

  11. A. Jain, A. Ross, S. Prabhakar, Fingerprint matching using minutia and texture features, in ICIP (2001)

    Google Scholar 

  12. J. Feng, S. Yoon, A.K. Jain, Latent fingerprint matching: Fusion of rolled and plain fingerprints, vol. 5558 (Springer Lecture Notes in Computer Science, 2009), pp. 695–704

    Google Scholar 

  13. M. Saad, Low-level color and texture feature extraction for content-based image retrieval, in Multi-Dimensional Digital Signal Processing, EE K 381 (2008), pp. 20–28

    Google Scholar 

  14. S. Selvarajah, S.R. Kodituwakku, Analysis and comparison of texture features for content based image retrieval. Int. J. Latest Trends Comput. 2 (2011)

    Google Scholar 

  15. J. Feng, Combining minutia descriptors for fingerprint matching. J. Pattern Recognit. 41, 342–352 (2008)

    Google Scholar 

  16. H. Choi, M. Boaventura, I.A. Boaventura, A.K. Jain, Automatic segmentation of latent fingerprints (BTAS, Washington, D.C., 2012), pp. 23–26

    Google Scholar 

  17. S. Yoon, J. Feng, A.K. Jain, Latent fingerprint enhancement via robust orientation field estimation. International Joint Conference on Biometrics (IJCB) (2011), pp. 1–8

    Google Scholar 

  18. S. Yoon, J. Feng, A.K. Jain, On latent fingerprint enhancement. in Proceedings of the SPIE Conference on Biometric Technology for Human Identification VII (2010), pp. 766–707

    Google Scholar 

  19. T. Chen, Q.H. Wu, R. Rahmani, R, A pseudo top-hat mathematical morphological approach to edge detection in dark regions. Pattern Recognit. 35(1), (2002)

    Google Scholar 

  20. D. Zorita, J. Ortega-Garcia, S. Cruz-Llanas, J. Sanchesz-Bote, J. Glez Rodriguez, An improved image enhancement scheme for fingerprint minutia extraction in biometric identification, vol. 2091, (Springer Lecture Notes in Computer Science, 2001), pp. 217–223

    Google Scholar 

  21. A.A. Paulino, J. Feng, A.K. Jain, Latent fingerprint matching using descriptor based Hough transform. IEEE Trans. Inf. Forensics Secur. 8(1) (2013)

    Google Scholar 

  22. NIST Special Database 27, Fingerprint Minutia from Latent and Matching Tenprint Images, http://www.nist.gov/srd/nistsd27.cfm

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Author information

Authors and Affiliations

  1. Department of CSE, Amrita School of Engineering, Amrita Nagar PO, Coimbatore, 641112, India

    V. Dhanusha & T. R. Swapna

Authors
  1. V. Dhanusha
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  2. T. R. Swapna
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Corresponding author

Correspondence to V. Dhanusha .

Editor information

Editors and Affiliations

  1. Electrical & Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    L Padma Suresh

  2. Electrical and Electronics Engineering, SRM Engineering College, Kattankulathur, Tamil Nadu, India

    Subhransu Sekhar Dash

  3. Electrical Engineering, IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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© 2015 Springer India

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Dhanusha, V., Swapna, T.R. (2015). Improving the Accuracy of Latent Fingerprint Matching Using Texture Descriptors. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 325. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2135-7_73

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  • DOI: https://doi.org/10.1007/978-81-322-2135-7_73

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2134-0

  • Online ISBN: 978-81-322-2135-7

  • eBook Packages: EngineeringEngineering (R0)

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