Skip to main content
SpringerLink
Log in

Fingerprint matching, spoof and liveness detection: classification and literature review

  • Review Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Fingerprint matching, spoof mitigation and liveness detection are the trendiest biometric techniques, mostly because of their stability through life, uniqueness and their least risk of invasion. In recent decade, several techniques are presented to address these challenges over well-known data-sets. This study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few decades. It divides the research on fingerprint into nine different approaches including feature based, fuzzy logic, holistic, image enhancement, latent, conventional machine learning, deep learning, template matching and miscellaneous techniques. Among these, deep learning approach has outperformed other approaches and gained significant attention for future research. By reviewing fingerprint literature, it is historically divided into four eras based on 106 referred papers and their cumulative citations.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kucken M, Newell A C. Fingerprint formation. Journal of Theoretical Biology, 2005, 235(1): 71–83

    MathSciNet  MATH  Google Scholar 

  2. Sengupta S, Sengupta T. Iris texture analysis, finger printing and signature verification for automatic data retrieval system using biometric pattern recognition system. International Journal of Advances in Computing and Management, 2013, 2(1): 6–10

    Google Scholar 

  3. McMahon D H, Johnson G L, Teeter S L, Whitney C G. A hybrid optical computer processing technique for fingerprint identification. IEEE Transactions on Computers, 1975, 100(4): 358–369

    Google Scholar 

  4. Jain A K, Prabhakar S, Pankanti S. On the similarity of identical twin fingerprints. Pattern Recognition, 2002, 35(11): 2653–2663

    MATH  Google Scholar 

  5. Maltoni D, Maio D, Jain A K, Prabhakar S. Handbook of Fingerprint Recognition. Springer Science & Business Media, 2009

  6. Cole S A. Is fingerprint identification valid? rhetorics of reliability in fingerprint proponents discourse. Law & Policy, 2006, 28(1): 109–135

    Google Scholar 

  7. Cole S A. Suspect Identities: A History of Fingerprinting and Criminal Identification. Harvard University Press, 2009

  8. Wang C N, Wang J W, Lin M H, Chang Y L, Kuo C M. Optical methods in fingerprint imaging for medical and personality applications. Journal of Sensors, 2017, 17(10): 2418

    Google Scholar 

  9. Jain A K, Ross A A, Nandakumar K. Introduction to Biometrics. Springer Science & Business Media, 2011

  10. Wayman J, Jain A, Maltoni D, Maio D. An Introduction to Biometric Authentication Systems. Springer Biometric Systems, 2005

  11. Medina-Pérez M A, García-Borroto M, Gutierrez-Rodrguez A E, Altamirano-Robles L. Improving fingerprint verification using minutiae triplets. Journal of Sensors, 2012, 12(3): 3418–3437

    Google Scholar 

  12. Sahasrabudhe M, Namboodiri A M. Fingerprint enhancement using un-supervised hierarchical feature learning. In: Proceedings of the ACM Indian Conference on Computer Vision Graphics and Image Processing. 2014, 14–17

  13. Jea T Y, Govindaraju V. A minutia-based partial fingerprint recognition system. Pattern Recognition, 2005, 38(10): 1672–1684

    Google Scholar 

  14. Msiza I S, Leke-Betechuoh B, Nelwamondo F V, Msimang N. A fingerprint pattern classification approach based on the coordinate geometry of singularities. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics. 2009, 510–517

  15. Jiang X, Ser W. Online fingerprint template improvement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(8): 1121–1126

    Google Scholar 

  16. Selvi M, George A. Fbfet: fuzzy based fingerprint enhancement technique based on adaptive thresholding. In: Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies. 2013, 1–5

  17. Kim J, Teoh A B J. One-factor cancellable biometrics based on indexing-first-order hashing for fingerprint authentication. In: Proceedings of the 24th International Conference on Pattern Recognition. 2018, 3108–3113

  18. Li P, Yang X, Cao K, Tao X, Wang R, Tian J. An alignment free fingerprint cryptosystem based on fuzzy vault scheme. Journal of Network and Computer Applications, 2010, 33(3): 207–220

    Google Scholar 

  19. Liu E, Zhao H, Liang J, Pang L, Xie M, Chen H, Li Y, Li P, Tian J. A key binding system based on n-nearest minutiae structure of fingerprint. Pattern Recognition Letters, 2011, 32(5): 666–675

    Google Scholar 

  20. Ferrara M, Maltoni D, Cappelli R. Noninvertible minutia cylinder-code representation. IEEE Transactions on Information Forensics and Security, 2012, 7(6): 1727–1737

    Google Scholar 

  21. Chen Z, Kuo C. A topology-based matching algorithm for fingerprint authentication. In: Proceedings of IEEE International Carnahan Conference on Security Technology. 1991, 84–87

  22. Bhanu B, Tan X. Fingerprint indexing based on novel features of minutiae triplets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 616–622

    Google Scholar 

  23. Singh R, Vatsa M, Noore A. Fingerprint indexing using minutiae and pore features. In: Proceedings of International Conference on Image Processing. 2009, 870–875

  24. Afsar F, Arif M, Hussain M. Fingerprint identification and verification system using minutiae matching. In: Proceedings of National Conference on Emerging Technologies. 2004, 141–146

  25. Jaam J M, Rebaiaia M L, Hasnah A. A fingerprint minutiae recognition system based on genetic algorithms. International Arab Journal of Information Technology, 2006, 3(3): 242–248

    Google Scholar 

  26. Zhao Q, Zhang L, Zhang D, Luo N. Direct pore matching for fingerprint recognition. In: Proceedings of International Conference on Biometrics. 2009, 597–606

  27. Zhao Q, Zhang D, Zhang L, Luo N. High resolution partial fingerprint alignment using pore-valley descriptors. Pattern Recognition, 2010, 43(3): 1050–1061

    MATH  Google Scholar 

  28. Sudiro S A, Yuwono R T. Adaptable fingerprint minutiae extraction algorithm based on crossing number method for hardware implementation using fpga device. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2012, 2(3): 1–30

    Google Scholar 

  29. Sadi M S, Uddin M N, Ahad A, Haque A. An efficient approach to recognize fingerprints. Journal of Multimedia, 2012, 7(5): 327–331

    Google Scholar 

  30. Madhuri R M, Mishra R. Fingerprint recognition using robust local features. International Journal of Advanced Research in Computer Science and Software Engineering, 2012, 2(6): 1–5

    Google Scholar 

  31. Ackerman A, Ostrovsky R. Fingerprint recognition. Technical Report, UCLA Computer Science Department, 2012

  32. Nagar A, Nandakumar K, Jain A K. A hybrid biometric cryptosystem for securing fingerprint minutiae templates. Pattern Recognition Letters, 2010, 31(8): 733–741

    Google Scholar 

  33. Feng J, Jain A K. Fingerprint reconstruction: from minutiae to phase. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 209–223

    Google Scholar 

  34. Yoon S, Feng J, Jain A J. Altered fingerprints: analysis and detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 451–464

    Google Scholar 

  35. Chugh T, Cao K, Jain A K. Fingerprint spoof buster: use of minutiae-centered patches. IEEE Transactions on Information Forensics and Security, 2018, 13(9): 2190–2202

    Google Scholar 

  36. Engelsma J J, Cao K, Jain A K. Raspireader: open source fingerprint reader. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(10): 2511–2524

    Google Scholar 

  37. Gottschlich C, Marasco E, Yang A Y, Cukic B. Fingerprint liveness detection based on histograms of invariant gradients. In: Proceedings of IEEE International Joint Conference on Biometrics. 2014, 1–7

  38. Rattani A, Akhtar Z, Foresti G. A preliminary study on identifying fabrication material from fake fingerprint images. In: Proceedings of IEEE Symposium Series on Computational Intelligence. 2015, 362–366

  39. Raghavendra R, Avinash M, Marcel S, Busch C. Finger vein liveness detection using motion magnification. In: Proceedings of the 7th IEEE International Conference on Biometrics Theory, Applications and Systems. 2015, 1–7

  40. Ding H. Anti-spoofing a finger vascular recognition device with pulse detection. In: Proceedings of the 24th Twente Student Conference on IT. 2016

  41. Newton S C, Pemmaraju S, Mitra S. Adaptive fuzzy leader clustering of complex data sets in pattern recognition. IEEE Transactions on Neural Networks, 1992, 3(5): 794–800

    Google Scholar 

  42. Velamuri R. Fingerprint recognition using fuzzy inferencing techniques. Technical Report, Fuzzy Logic and Engineering, University of Texas El Paso, 2006

  43. Yildirim M T, Basturk A, Yuksel M E. A detail preserving type-2 fuzzy logic filter for impulse noise removal from digital images. In: Proceedings of IEEE International Conference on Fuzzy Systems. 2007, 1–6

  44. Majumdar A, Ward R. Fingerprint recognition with curvelet features and fuzzy KNN classifier In: Proceedings of the 10th IASTED International Conference. 2008, 31–36

  45. Jain A K, Prabhakar S, Hong L, Pankanti S. Filterbank-based fingerprint matching. IEEE Transactions on Image Processing, 2000, 9(5): 846–859

    Google Scholar 

  46. Watson C I, Grother P J, Casasent D P. Distortion-tolerant filter for elastic-distorted fingerprint matching. In: Proceedings of SPIE, Optical Pattern Recognition XI. 2000, 166–174

  47. Jung W H. Fast fingerprint recognition using spiral. Technical Report, Department of Electrical and Computer Engineering, Carnegie Mellon University, 2005

  48. Xu H, Veldhuis R N, Bazen A M, Kevenaar T A, Akkermans T A, Gokberk B. Fingerprint verification using spectral minutiae representations. IEEE Transactions on Information Forensics and Security, 2009, 4(3): 397–409

    Google Scholar 

  49. Aburas A A, Rehiel S A. Fingerprint patterns recognition system using huffman coding. In: Proceedings of the World Congress on Engineering. 2008, 1794–1796

  50. Chen F, Feng J, Jain A K, Zhou J, Zhang J. Separating overlapped fingerprints. IEEE Transactions on Information Forensics and Security, 2011, 6(2): 346–359

    Google Scholar 

  51. Sherlock B G, Monro D, Millard K. Fingerprint enhancement by directional fourier filtering. IEE Proceedings Vision, Image and Signal Processing, 1994, 147(2): 87–94

    Google Scholar 

  52. Hong L, Wan Y, Jain A K. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 777–789

    Google Scholar 

  53. Bansal R, Arora P, Gaur M, Sehgal P, Bedi P. Fingerprint image enhancement using type-2 fuzzy sets. In: Proceedings of the 6th IEEE International Conference on Fuzzy Systems and Knowledge Discovery. 2009, 412–417

  54. Chawla V, Kant C. Addressing sensor interoperability problem using fingerprint segmentation. International Journal of Advanced Research in Computer Science, 2016, 7(3): 125–131

    Google Scholar 

  55. Feng J, Zhou J, Jain A K. Orientation field estimation for latent fingerprint enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4): 925–940

    Google Scholar 

  56. Cao K, Liu E, Jain A K. Segmentation and enhancement of latent fingerprints: a coarse to fine ridge structure dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(9): 1847–1859

    Google Scholar 

  57. Ito K, Morita A, Aoki T, Higuchi T, Nakajima H, Kobayashi K. A fingerprint recognition algorithm using phase-based image matching for low-quality fingerprints. In: Proceedings of IEEE International Conference on Image Processing. 2005, 33–36

  58. Mil’Shtein S, Pillai S, Shendye A, Liessner C, Baier M. Fingerprint recognition algorithms for partial and full fingerprints. In: Proceedings of IEEE Conference on Technologies for Homeland Security. 2008, 449–452

  59. Jain A K, Feng J. Latent fingerprint matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(1): 88–100

    Google Scholar 

  60. Vatsa M, Singh R, Noore A, Morris K. Simultaneous latent fingerprint recognition. Journal of Applied Soft Computing, 2011, 11(7): 4260–4266

    Google Scholar 

  61. Nagar A, Choi H, Jain A K. Evidential value of automated latent fingerprint comparison: an empirical approach. IEEE Transactions on Information Forensics and Security, 2012, 7(6): 1752–1765

    Google Scholar 

  62. Zhao Q, Jain A K. Model based separation of overlapping latent fingerprints. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 904–918

    Google Scholar 

  63. Paulino A A, Feng J, Jain A K. Latent fingerprint matching using descriptor-based hough transform. IEEE Transactions on Information Forensics and Security, 2012, 8(1): 31–45

    Google Scholar 

  64. Arora S S, Liu E, Cao K, Jain A K. Latent fingerprint matching: performance gain via feedback from exemplar prints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(12): 2452–2465

    Google Scholar 

  65. Chugh T, Cao K, Zhou J, Tabassi E, Jain A K. Latent fingerprint value prediction: crowd-based learning. IEEE Transactions on Information Forensics and Security, 2018, 13(1): 20–34

    Google Scholar 

  66. Leung M T, Engeler W, Frank P. Fingerprint image processing using neural networks. In: Proceedings of IEEE Region 10 Conference on Computer and Communication Systems. 1990, 582–586

  67. Wilson C L, Candela G T, Watson C I. Neural network fingerprint classification. Journal of Artificial Neural Networks, 1994, 1(2): 203–228

    Google Scholar 

  68. Jain A K, Prabhakar S, Hong L. A multichannel approach to fingerprint classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(4): 348–359

    Google Scholar 

  69. Tan B, Lewicke A, Yambay D, Schuckers S. The effect of environmental conditions and novel spoofing methods on fingerprint anti-spoofing algorithms. In: Proceedings of IEEE International Workshop on Information Forensics and Security. 2010, 1–6

  70. Rattani A, Ross A. Automatic adaptation of fingerprint liveness detector to new spoof materials. In: Proceedings of IEEE International Joint Conference on Biometrics. 2014, 1–8

  71. Ding Y, Rattani A, Ross A. Bayesian belief models for integrating match scores with liveness and quality measures in a fingerprint verification system. In: Proceedings of IEEE International Conference on Biometrics. 2016, 1–8

  72. Liu E, Zhao H, Guo F, Liang J, Tian J. Fingerprint segmentation based on an adaboost classifier. Frontiers of Computer Science, 2011, 5(2): 148–157

    MathSciNet  Google Scholar 

  73. Kristensen T. Two different regimes of fingerprint identification-a comparison. American Journal of Computational and Applied Mathematics, 2012, 2(2): 1–9

    MathSciNet  Google Scholar 

  74. Minaee S, Wang Y. Fingerprint recognition using translation invariant scattering network. In: Proceedings of IEEE Symposium on Signal Processing in Medicine and Biology. 2015, 1–6

  75. Menotti D, Chiachia G, Pinto A, Schwartz W R, Pedrini H, Falcao A X, Rocha A. Deep representations for iris, face, and fingerprint spoofing detection. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 864–879

    Google Scholar 

  76. Ryu C, Kim H, Jain A K. Template adaptation based fingerprint verification. In: Proceedings of the 18th International Conference on Pattern Recognition. 2006, 582–585

  77. Cappelli R, Lumini A, Maio D, Maltoni D. Evaluating minutiae template vulnerability to masquerade attack. In: Proceedings of IEEE Workshop on Automatic Identification Advanced Technologies. 2007, 174–179

  78. Cappelli R, Maio D, Lumini A, Maltoni D. Fingerprint image reconstruction from standard templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(9): 1489–1503

    Google Scholar 

  79. Thai L H, Tam H N. Fingerprint recognition using standardized fingerprint model. International Journal of Computer Science, 2010, 7(7): 1694–1700.

    Google Scholar 

  80. Gao Q. Fingerprint based authentication with randomly selected minutiae sub-templates. International Journal of Research and Reviews in Information Security and Privacy (IJRRISP), 2012, 2(1): 55–62

    Google Scholar 

  81. Jiang P, Wen Q, Li W, Jin Z, Zhang H. An anonymous and efficient remote biometrics user authentication scheme in a multi server environment. Frontiers of Computer Science, 2015, 9(1): 142–156

    MathSciNet  Google Scholar 

  82. Ratha N K, Connell J H, Bolle R M. An analysis of minutiae matching strength. In: Proceedings of International Conference on Audio and Video Based Biometric Person Authentication. 2001, 223–228

  83. Vatsa M, Singh R, Noore A, Houck M M, Morris K. Robust biometric image watermarking for fingerprint and face template protection. IEICE Electronics Express, 2006, 3(2): 23–28

    Google Scholar 

  84. Fierrez-Aguilar J, Chen Y, Ortega-Garcia J, Jain A K. Incorporating image quality in multi-algorithm fingerprint verification. In: Proceedings of International Conference on Biometrics. 2006, 213–220

  85. He M, Zhao H. A identity authentication based on fingerprint identification. In: Proceedings of the International Symposium on Web Information Systems and Applications. 2009, 261–263

  86. Kellman P J, Mnookin J L, Erlikhman G, Garrigan P, Ghose T, Mettler E, Charlton D, Dror I E. Forensic comparison and matching of fingerprints using quantitative image measures for estimating error rates through understanding and predicting difficulty. PloS One, 2014, 9(5): e94617.

    Google Scholar 

  87. Yoon S, Jain A K. Longitudinal study of fingerprint recognition. Proceedings of the National Academy of Sciences, 2015, 112(28): 8555–8560

    Google Scholar 

  88. Jain A K, Arora S S, Cao K, Best-Rowden L, Bhatnagar A. Fingerprint recognition of young children. IEEE Transactions on Information Forensics and Security, 2017, 12(7): 1501–1514

    Google Scholar 

  89. Li X, Yin Y, Ning Y, Yang G, Pan L. A hybrid biometric identification framework for high security applications. Frontiers of Computer Science, 2015, 9(3): 392–401

    Google Scholar 

  90. Engeler W, Frank P, Leung M. Fingerprint image processing using neural network. In: Proceedings of IEEE Conference on Computer and Communication Systems. 1990, 582–586

  91. Jain A, Pankanti S. Automated fingerprint identification and imaging systems. Advances in Fingerprint Technology, 2001, 275–340

  92. Gu J, Zhou J, Yang C. Fingerprint recognition by combining global structure and local cues. IEEE Transactions on Image Processing, 2006, 15(7): 1952–1964

    Google Scholar 

  93. Cappelli R, Maio D, Maltoni D, Wayman J L, Jain A K. Performance evaluation of fingerprint verification systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(1): 3–18

    Google Scholar 

  94. Kekre H, Bharadi V. Fingerprint core point detection algorithm using orientation field based multiple features. International Journal of Computer Applications, 2010, 1(15): 106–112

    Google Scholar 

  95. Jain A K, Feng J, Nandakumar K. Fingerprint matching. Journal of Computer, 2010, 43(2): 36–44

    Google Scholar 

  96. Bana S, Kaur D D. Fingerprint recognition using image segmentation. International Journal of Advanced Engineering Sciences and Technologies, 2011, 5(1): 012–023

    Google Scholar 

  97. Mishra R, Mishra R. Fingerprint recognition using robust local features. International Journal of Advanced Research in Computer Science and Software Engineering, 2012, 2(6): 1–5

    Google Scholar 

  98. Subban R, Mankame D P. A study of biometric approach using fingerprint recognition. Lecture Notes on Software Engineering, 2013, 1(2): 209–213

    Google Scholar 

  99. Galbally J, Marcel S, Fierrez J. Image quality assessment for fake bio-metric detection: application to iris, fingerprint, and face recognition. IEEE Transactions on Image Processing, 2014, 23(2): 710–724

    MathSciNet  MATH  Google Scholar 

  100. Sankaran A, Vatsa M, Singh R. Latent fingerprint matching: a survey. IEEE Access, 2014, 2(1): 982–1004

    Google Scholar 

  101. Tiwari K, Kaushik V D, Gupta P. An efficient fingerprint matching using continuous minutiae template learning. In: Bhatia S, Tiwari S, Mishra K, Trivedi M, eds. Advances in Computer Communication and Computational Sciences. Springer, Singapore, 2019, 289–298

    Google Scholar 

  102. Jain A K, Nandakumar K, Ross A. 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recognition Letters, 2016, 79: 80–105

    Google Scholar 

  103. Ghiani L, Yambay D A, Mura V, Marcialis G L, Roli F, Schuckers S A. Review of the fingerprint liveness detection (livdet) competition series: 2009 to 2015. Image and Vision Computing, 2017, 58: 110–128

    Google Scholar 

  104. Manickam A, Devarasan E, Manogaran G, Priyan M K, Varatharajan R, Hsu C H, Krishnamoorthi R. Score level based latent fingerprint enhancement and matching using sift feature. Multimedia Tools and Applications, 2019, 78(3): 3065–3085

    Google Scholar 

  105. Khongkraphan K. An efficient fingerprint matching by multiple reference points. Journal of Information Processing Systems, 2019, 15(1): 22–33

    Google Scholar 

  106. Maio D, Maltoni D, Cappelli R, Wayman J L, Jain A K. Fvc2000: fingerprint verification competition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 402–412

    Google Scholar 

  107. Mansfield A J, Wayman J L. Best practices in testing and reporting performance of biometric devices. National Physical Labrotary Report, 2002, 1–32

  108. Maio D, Maltoni D, Cappelli R, Wayman J L, Jain A K. FVC2002: second fingerprint verification competition. In: Proceedings of IEEE Object Recognition Supported by User Interaction for Service Robots, 2002, 811–814

  109. Fierrez J, Ortega-Garcia J, Toledano D T, Gonzalez-Rodriguez J. Biosec baseline corpus: a multimodal biometric database. Pattern Recognition, 2007, 40(4): 1389–1392

    MATH  Google Scholar 

Download references

Acknowledgements

We are very grateful to Dr. Umar Suleman for his support and guidance. We also like to extend our gratitude to Sundas Ayaz, Afnan Muneer, Hafiza Anam Atique and Hafsa Mateen for their assistance. We are grateful to all the anonymous reviewers for their useful comments. This work was supported by the National ICT R&D (NICTRDF/NGIRI/2012-13/Corsp/3), and University of Management & Technology, Pakistan.

Author information

Authors and Affiliations

  1. Department of Software Engineering, University of Management and Technology, Lahore, 54770, Pakistan

    Syed Farooq Ali

  2. Department of Informatics and Systems, University of Management and Technology, Lahore, 54770, Pakistan

    Muhammad Aamir Khan

  3. Department of Computer Science, University of Management and Technology, Lahore, 54770, Pakistan

    Ahmed Sohail Aslam

Authors
  1. Syed Farooq Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Aamir Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed Sohail Aslam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Farooq Ali.

Additional information

Syed Farooq Ali received his PhD (CS) from UMT, Pakistan. He did his PhD course work, PhD Comprehensive exam and MS (CS) from Ohio State University, USA. He also completed his MS (CS) from LUMS, Pakistan with Deans Honor List. During his stay in MS, he was on LUMS fellowship. He is currently working as an Assistant Professor, UMT. His research interest includes computer vision, digital image processing and medical imaging. He is a reviewer for various IEEE conferences and journals.

Muhammad Aamir Khan is currently working as an assistant professor in the School of Systems and Technology (SST) at the University of Management and Technology Lahore, Pakistan. He holds a PhD in electronic engineering from the University of Twente, the Netherlands and three master degrees in System-on-Chip Design, Systems Engineering (Control Engineering) and Physics (Electronics) from the Royal Institute of Technology (KTH), Sweden, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad and University of the Punjab (PU), Lahore respectively. He has several years of experience in research and development including experience of working in reputed international and local organizations at senior positions. His research interest includes Electronic Design for DSP, Control/Communication, and Image Processing Applications.

Ahmed Sohail Aslam received his BSc in Electrical Engineering from University of Engineering & Technology, Pakistan in 1998 and MSc in Computer Science from University of South Florida, USA in 2006. He is currently working as an Assistant Professor in School of Systems and Technology, University of Management & Technology, Pakistan. His research interests include biometrics, digital image processing, and computer networks.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, S.F., Khan, M.A. & Aslam, A.S. Fingerprint matching, spoof and liveness detection: classification and literature review. Front. Comput. Sci. 15, 151310 (2021). https://doi.org/10.1007/s11704-020-9236-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-020-9236-4

Keywords

Search

Navigation