, 44:161 | Cite as

Towards smartphone-based touchless fingerprint recognition

  • Parmeshwar BirajadarEmail author
  • Meet Haria
  • Pranav Kulkarni
  • Shubham Gupta
  • Prasad Joshi
  • Brijesh Singh
  • Vikram Gadre


The widely used conventional touch-based fingerprint identification system has drawbacks like the elastic deformation due to nonuniform pressure, fingerprints collection time and hygiene. To overcome these drawbacks, recently the touchless fingerprint technology is gaining popularity and various touchless fingerprint acquisition solutions have been proposed. Nowadays due to the wide use of the smartphone in various biometric applications, smartphone-based touchless fingerprint systems using an embedded camera have been proposed in the literature. These touchless fingerprint images are very different from conventional ink-based and live-scan fingerprints. Due to varying contrast, illumination and magnification, the existing touch-based fingerprint matchers do not perform well while extracting reliable minutiae features. A touchless fingerprint recognition system using a smartphone is proposed in this paper, which incorporates a novel monogenic-wavelet-based algorithm for enhancement of touchless fingerprints using phase congruency features. For the comparative performance analysis of our system, we created a new touchless fingerprint database using the developed android app and this is publicly made available along with its corresponding live-scan images for further research. The experimental results in both verification and identification mode on this database are obtained using three widely used touch-based fingerprint matchers. The results show a significant improvement in Rank-1 accuracy and equal error rate (EER) achieved using the proposed system and the results are comparable to that of the touch-based system.


Biometrics touchless fingerprint recognition monogenic wavelet phase congruency fingerprint enhancement android app 



This work was supported by the NCETIS (National Center of Excellence in Technology for Internal Security) and MHRD-TEQIP-KITE, a TEQIP initiative of the Ministry of Human Resource Development at IIT Bombay. The authors would also like to thank the students of IIT Bombay, for helping them create the touchless fingerprint database. They also wish to acknowledge the active participation and support of Shri Balsing Rajput and Shri Deepak Dhole of Department of Cyber Maharashtra, Mumbai.


  1. 1.
    Maltoni D, Maio D and Jain A 2009 Handbook of fingerprint recognition, 2nd ed. New York: Springer-Verlag New York IncCrossRefGoogle Scholar
  2. 2.
    Aadhaar [online] Unique Identification Authority of India.
  3. 3.
    Labati R, Piuri V and Scotti F 2015 Touchless fingerprint biometrics, 1st ed. Boca Raton, FL, USA: CRC PressCrossRefGoogle Scholar
  4. 4.
  5. 5.
    Si X et al 2015 Detection and rectification of distorted fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 37(3): 555–568CrossRefGoogle Scholar
  6. 6.
    Birajadar P et al 2016 Touchless fingerphoto feature extraction, analysis and matching using monogenic wavelets. In: Proceedings of the IEEE International Conference on Signal and Information Processing (IConSIP), Nanded, October, pp. 1–7Google Scholar
  7. 7.
    Labati R et al 2014 Touchless fingerprint biometrics: a survey on 2D and 3D technologies. J. Internet Technol. 15(3): 325–332Google Scholar
  8. 8.
    Kumar A and Kwong C 2015 Towards contactless, low-cost and accurate 3D fingerprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 37(3): 681–696CrossRefGoogle Scholar
  9. 9.
    Sankaran A et al 2015 On smartphone camera based fingerphoto authentication. In: Proceedings of the 7th IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS), September, pp. 1–7Google Scholar
  10. 10.
    Stein C, Nickel C and Busch C 2012 Fingerphoto recognition with smartphone cameras. In: Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), September, pp. 1–12Google Scholar
  11. 11.
    Tiwari K and Gupta P 2015 A touchless fingerphoto recognition system for mobile hand-held devices. In: Proceedings of the International Conference on Biomertrics (ICB), May, pp. 151–156Google Scholar
  12. 12.
    Yang B et al 2012 Collecting fingerprints for recognition using mobile phone cameras. Proceedings of SPIE 8304: Multimedia on Mobile Devices Google Scholar
  13. 13.
    Bruna J and Mallat S 2013 Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1872–1886CrossRefGoogle Scholar
  14. 14.
    Cheng K and Kumar A 2012 Contactless finger knuckle identification using smartphones. In: Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1–6Google Scholar
  15. 15.
    Putra G et al 2014 Android based palmprint recognition system. TELKOMNIKA 12(1): 263–272CrossRefGoogle Scholar
  16. 16.
    Raja K et al 2015 Smartphone based visible iris recognition using deep sparse filtering. Pattern Recognit. Lett. 57(1): 33–42CrossRefGoogle Scholar
  17. 17.
    Son B et al 2015 Method of recognizing contactless fingerprint and electronic device for performing the same. US Patent 0146943, MayGoogle Scholar
  18. 18.
    TBS [online]. Available:
  19. 19.
  20. 20.
    Diamondfortress [online]. Available:
  21. 21.
    Feng J and Jain A 2011 Fingerprint reconstruction: from minutiae to phase. IEEE Trans. Pattern Anal. Mach. Intell. 33(2): 209–223CrossRefGoogle Scholar
  22. 22.
    Oppenheim A and Lim J 1981 The importance of phase in signals. Proc. IEEE 69(5): 529–541CrossRefGoogle Scholar
  23. 23.
    Gabor D 1946 Theory of communication. Part 1: the analysis of information. J. Inst. Electr. Eng. 93(26): 429–441Google Scholar
  24. 24.
    Stein E and Weiss G 1971 Introduction to Fourier analysis on Euclidean spaces. Princeton, NJ: Princeton University PresszbMATHGoogle Scholar
  25. 25.
    Kovesi P 2003 Phase congruency detects corners and edges. In: Proceedings of the Australian Pattern Recognition Society Conference, DICTA Australia, pp. 309–318Google Scholar
  26. 26.
    Gadre V and Abhayankar A 2017 Multiresolution and multirate signal processing, 1st ed. McGraw Hill Education, Noida, Uttar Pradesh, IndiaGoogle Scholar
  27. 27.
    Bulow T and Sommer G 2001 Hypercomplex signals—a novel extension of the analytic signal to the multidimensional case. IEEE Trans. Signal Process. 49(11): 2844–2852MathSciNetCrossRefGoogle Scholar
  28. 28.
    Felsberg M and Sommer G 2001 The monogenic signal. IEEE Trans. Signal Process. 49(12): 3136–3144MathSciNetCrossRefGoogle Scholar
  29. 29.
    Unser M et al 2009 Multiresolution monogenic signal analysis using the Riesz–Laplace wavelet transform. IEEE Trans. Image Process. 18(11): 2402–2418MathSciNetCrossRefGoogle Scholar
  30. 30.
    Olhede S and Metikas G 2009 The monogenic wavelet transform. IEEE Trans. Signal Process. 57(9): 3426–3441MathSciNetCrossRefGoogle Scholar
  31. 31.
    Canny J 1986 Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6): 679–698CrossRefGoogle Scholar
  32. 32.
    Aw Y et al 1998 An analysis of local energy and phase congruency models in visual feature detection. J. Australian Math. Soc. Ser. B 40(1): 97–122MathSciNetCrossRefGoogle Scholar
  33. 33.
    Venkatesh S and Owens R 1989 An energy feature detection scheme. In: Proceedings of the International Conference on Image Processing, Singapore, pp. 553–557Google Scholar
  34. 34.
    Android SDK [online]. Available:
  35. 35.
    Eclipse JAVA IDE [online]. Available:
  36. 36.
    Neurotechnology Inc. [online] Verifinger SDK. Available:
  37. 37.
    XAMPP Apache Distribution [online]. Available:
  38. 38.
    Vanzan R SourceAFIS [online]. Available:
  39. 39.
  40. 40.
    Birajadar P et al 2016 A novel iris recognition technique using monogenic wavelet phase encoding. In: Proceedings of the IEEE International Conference on Signal and Information Processing (IConSIP), Nanded, October, pp. 1–6Google Scholar
  41. 41.
    Soulard R et al 2013 Vector extension of monogenic wavelets for geometric representation of color images. IEEE Trans. Image Process. 22(3): 1070–1083MathSciNetCrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology BombayMumbaiIndia
  2. 2.Department of Cyber MaharashtraMumbaiIndia

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