Skip to main content
SpringerLink
Log in

Human Identification System Based on Latent Fingerprint

  • Conference paper
  • First Online:
Communication and Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

Abstract

Latent fingerprint is a very sensitive topic in digital forensic as a final verdict can be given to the criminal on its basis.  This sensitiveness is due to two main reasons: (1) uniqueness of fingerprint in every human existence is proven to be a universal truth, and (2) latent fingerprint is a type of fingerprint which is not visible through the necked eye and gets deposited to the surface by the person unintentionally, telling the story of his/her presence. From the past 50 years, latent is used as human identification in forensics. These identification phases have many simultaneous steps such as data collection, preprocessing, extraction, matching and decision. Earlier these steps are performed manually but gradually the world moved to the automatic system with invent of technology. Since then there is a lot of evaluation of techniques, methods or algorithms, it happens in different aspects of identification system phases. One of the steps is the extraction phase, which includes three levels. There are a lot of existing models based on Level-1 (features: ridge pattern) and Level-2 (feature: minutiae) but very few have worked in the field of Level-3 (Features: pores) feature extraction. In this paper, we have described an identification framework which uses pores-based feature extraction. This method is compared with some other popular extraction methods like inversion and Gabor filter. Results of experiments in Tables 1 and 2 show that the performance rate of CNN is better then inverse and Gabor filter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sankaran A, Mayank V, Richa S (2014) Latent fingerprint matching: a survey. IEEE access. IEEE Biometric Conpendium RFIC Vitual J 2:982–1004

    Google Scholar 

  2. Li J, Feng J, Jay Kuo C-C (2018) Deep convolutional neural network for latent fingerprint enhancement. Signal Process Image Commun 60:52–63

    Article  Google Scholar 

  3. Cao K, Jain AK (2018) Automated latent fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 41(4):788–800

    Article  Google Scholar 

  4. Cao K, Jain AK (2018) Latent fingerprint recognition: role of texture template. In: 2018 IEEE 9th international conference on biometrics theory, applications and systems (BTAS). IEEE

    Google Scholar 

  5. Manickam A et al (2019) Score level based latent fingerprint enhancement and matching using SIFT feature. Multimedia Tools Appl 78(3):3065–3085

    Article  Google Scholar 

  6. Maltoni D et al (2009) Handbook of fingerprint recognition. Springer Science & Business Media

    Google Scholar 

  7. Zhao Q, Feng J, Jain AK (2010) Latent fingerprint matching: Utility of level 3 features. MSU Techical Report 8:1–30

    Google Scholar 

  8. Maltoni D, Cappelli R, Meuwly D (2017) Automated fingerprint identification systems: from fingerprints to fingermarks. In: Handbook of biometrics for forensic science. Springer, pages 37–61

    Google Scholar 

  9. Oig A (2006) Review of the fbi’s handling of the brandon mayfield case. Office of the Inspector General, Oversight and Review Division, US Department of Justice, pp 1–330

    Google Scholar 

  10. Stosz JD, Alyea LA (1994) Automated system for fingerprint authentication using pores and ridge structure. In: Automatic systems for the identification and inspection of humans, volume 2277. International Society for Optics and Photonics, pp 210–223

    Google Scholar 

  11. Kryszczuk K, Drygajlo A, Morier P (2004) Extraction of level 2 and level 3 features for fragmentary fingerprints. In: Proceedings of 2nd COST275 workshop, volume 8388

    Google Scholar 

  12. Kryszczuk KM, Morier P, Drygajlo A (2004) Study of the distinctiveness of level 2 and level 3 features in fragmentary fingerprint comparison. In: International workshop on biometric authentication. Springer, pp 124–133

    Google Scholar 

  13. Ray M, Meenen P, Adhami R (2005) A novel approach to fingerprint pore extraction. In: Proceedings of the thirty-seventh southeastern symposium on system theory, 2005. SSST’05.. IEEE, pp 282–286

    Google Scholar 

  14. Jain A, Chen Y, Demirkus M (2006) Pores and ridges: Fingerprint matching using level 3 features. In: 18th international conference on pattern recognition (ICPR’06), vol 4. IEEE, pp 477–480

    Google Scholar 

  15. Parsons NR, Smith JQ, Thönnes E, Wang L, Wilson RG (2008) Rotationally invariant statistics for examining the evidence from the pores in fingerprints. Law Probab Risk 7(1):1–14

    Article  Google Scholar 

  16. Zhao Q, Zhang D, Zhang L, Luo N (2010) Adaptive fingerprint pore modeling and extraction. Pattern Recogn 43(8):2833–2844

    Article  Google Scholar 

  17. Genovese A, Munoz E, Piuri V, Scotti F, Sforza G (2016) Towards touchless pore fingerprint biometrics: a neural approach. In: 2016 IEEE congress on evolutionary computation (CEC), . IEEE, pp 4265–4272

    Google Scholar 

  18. Jang H-U, Kim D, Mun S-M, Choi S, Lee H-K (2017) Deeppore: fingerprint pore extraction using deep convolutional neural networks. IEEE Sig Process Lett 24(12):1808–1812

    Article  Google Scholar 

  19. Labati RD, Genovese A, Muñoz E, Piuri V, Scotti F (2018) A novel pore extraction method for heterogeneous fingerprint images using convolutional neural networks. Pattern Recogn Lett 113:58–66

    Article  Google Scholar 

  20. Nguyen D-L, Jain AK (2019) End-to-end pore extraction and matching in latent fingerprints: going beyond minutiae. arXiv preprint arXiv:1905.11472

  21. Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: International conference on machine learning, pp 1139–1147

    Google Scholar 

  22. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernet 9(1):62–66

    Article  MathSciNet  Google Scholar 

  23. Watson CI, Garris MD, Tabassi E, Wilson CL, Michael McCabe R, Janet S, Ko K (2007) User’s guide to nist biometric image software (nbis)

    Google Scholar 

  24. Loy G, Zelinsky A (2003) Fast radial symmetry for detecting points of interest. IEEE Trans Pattern Anal Mach Intell 25(8):959–973

    Article  Google Scholar 

  25. Jensen TR, Toft B (2011) Graph coloring problems, vol 39. Wiley, New York

    MATH  Google Scholar 

  26. Pardalos PM, Mavridou T, Xue J (1998) The graph coloring problem: a bibliographic survey. In: Handbook of combinatorial optimization. Springer, pp 1077–1141

    Google Scholar 

  27. Duda RO, Hart PE (2001) Dg stork pattern classification. Wiley, New York

    Google Scholar 

  28. Zhao Q, Liu F, Zhang L, Zhang D (2010) Parallel versus hierarchical fusion of extended fingerprint features. In: 2010 20th international conference on pattern recognition. IEEE, pp 1132–1135

    Google Scholar 

  29. Yuri G, Saharon S (1987) Expected computation time for hamiltonian path problem. SIAM J Comput 16(3):486–502

    Article  MathSciNet  Google Scholar 

  30. Castellani U, Cristani M, Fantoni S, Murino V (2008) Sparse points matching by combining 3d mesh saliency with statistical descriptors. In: Computer graphics forum, vol 27. Wiley Online Library, pp 643–652

    Google Scholar 

Download references

Acknowledgement

This research is completed with the support of Information Security Education and Awareness Project (ISEA), Project Phase-II,MeitY, Government of India.

Author information

Authors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, 800005, India

    Shashi Shreya & Kakali Chatterjee

Authors
  1. Shashi Shreya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kakali Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashi Shreya .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Jaipur, India

    Mukesh Kumar Gupta

  3. Birla Institute of Applied Sciences, Nainital, Uttarakhand, India

    G. S. Tomar

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Wang Lipo

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shreya, S., Chatterjee, K. (2021). Human Identification System Based on Latent Fingerprint. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_69

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-1089-9_69

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1088-2

  • Online ISBN: 978-981-16-1089-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation