Cluster Computing

, Volume 22, Supplement 5, pp 12147–12155 | Cite as

A comparative analysis of similarity distance measure functions for biocryptic authentication in cloud databases

  • R. RamyaEmail author
  • T. Sasikala


Biometric recognition identifies an individual with the help of biological traits of humans like fingerprint, iris, voice etc. Estimation of similarity between the biometric images stored in cloud databases needs a closer attention due to its essentiality. To improve the privacy concerns, the proposed system encrypts the biometric data via feature extracted data hashing with the private key. The minutiae feature extraction procedure adequately removes edge detected features from the sensed data. The keys generated are stored in the cloud database. This work focuses on the comparison of the various distance similarity measure functions used for matching the stored template key with the query key. It has been found that by comparing with many existing similarity distance measures Minkowski distance method performs better in terms of accuracy, recall and F-score. Since the work involves fingerprint as the biometric trait, the performance is measured using false matching rate, false non-matching rate, false positive identification rate, false negative identification rate, accuracy, recall, F-score. The implementation has been done with MATLAB.


Cloud database Similarity distance measure Minkowski distance method Hashing technique 



I would like to take the opportunity to thank the person who guided and supported me during this process. I have a great pleasure in expressing my deep sense of gratitude and indebtedness to Dr. T. Sasikala, my Supervisor for her continuous guidance and invaluable suggestion at all the times during the research work.


  1. 1.
    Muhammad, G.: Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Clust. Comput. 18(2), 795–802 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Lee, S., Hooshyar, D., Ji, H., Nam, K., Lim, H.: Mining biometric data to predict programmer expertise and task difficulty. Clust. Comput. 2017, 1–11 (2017)Google Scholar
  3. 3.
    Van Heel, M.: Similarity measures between images. Ultramicroscopy 21(1), 95–100 (1987)CrossRefGoogle Scholar
  4. 4.
    Sturn, A.: Cluster analysis for large scale gene expression studies. Doctoral dissertation, Graz University of TechnologyGoogle Scholar
  5. 5.
    Yampolskiy, R.V., Govindaraju, V.: Use of behavioral biometrics in intrusion detection and online gaming. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series (2006)Google Scholar
  6. 6.
    Mahalanobis distance [Internet]. Accessed 25 July 2015
  7. 7.
    Norouzi, B., Seyedzadeh, S.M., Mirzakuchaki, S., Mosavi, M.R.: A novel image encryption based on hash function with only two-round diffusion process. Multimed. Syst. 20(1), 45–64 (2014)CrossRefGoogle Scholar
  8. 8.
    Al-Riyami, A., Zhang, N., Keane, J.: Impact of hash value truncation on ID anonymity in wireless sensor networks. Ad Hoc Netw. 45(1), 80–103 (2016)CrossRefGoogle Scholar
  9. 9.
    Wan, J., Tang, S., Zhang, Y., Li, J., Wu, P., Hoi, S.C.: HDIdx: high-dimensional indexing for efficient approximate nearest neighbor search. Neurocomputing 1, 401–404 (2016)Google Scholar
  10. 10.
    Sun, R., Zeng, W.: Secure and robust image hashing via compressive sensing. Multimed. Tools Appl. 70(3), 1651–1665 (2014)CrossRefGoogle Scholar
  11. 11.
    Kavsaoğlu, A.R., Polat, K., Bozkurt, M.R.: A novel feature ranking algorithm for biometric recognition with PPG signals. Comput. Biol. Med. 49, 1–14 (2014)CrossRefGoogle Scholar
  12. 12.
    Tome, P., Fierrez, J., Vera-Rodriguez, R., Nixon, M.S.: Soft biometrics and their application in person recognition at a distance. IEEE Trans. Inf. Forensics Secur. 9(3), 464–475 (2014)CrossRefGoogle Scholar
  13. 13.
    Telgad, R.L., Deshmukh, P.D., Siddiqui, A.M.: Combination approach to score level fusion for Multimodal Biometric system by using face and fingerprint. In: Recent Advances and Innovations in Engineering (ICRAIE), vol. 1, pp. 1–8. IEEE(2014)Google Scholar
  14. 14.
    Jiao, L., Tang, X., Hou, B., Wang, S.: SAR images retrieval based on semantic classification and region-based similarity measure for earth observation. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 8(8), 3876–3891 (2015)Google Scholar
  15. 15.
    Gupta, P., Gupta, P.: An accurate finger vein based verification system. Digit. Signal Proc. 38(1), 43–52 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ozturk, C., Hancer, E., Karaboga, D.: Dynamic clustering with improved binary artificial bee colony algorithm. Appl. Soft Comput. 28(1), 69–80 (2105)Google Scholar
  17. 17.
    Doroz, R., Porwik, P., Orczyk, T.: Dynamic signature verification method based on association of features with similarity measures. Neurocomputing 171, 921–931 (2016)CrossRefGoogle Scholar
  18. 18.
    Petrou, Z.I., Tian, Y.: High-resolution sea ice motion estimation with optical flow using satellite spectroradiometer data. IEEE Trans. Geosci. Remote Sens. 55, 1339–1350 (2016)CrossRefGoogle Scholar
  19. 19.
    Wang, C., Qin, S.: Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis. Infrared Phys. Technol. 69(1), 123–135 (2015)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Topcu, B., Karabat, C., Azadmanesh, M., Erdogan, H.: Practical security and privacy attacks against biometric hashing using sparse recovery. EURASIP J. Adv. Signal Process. 1, 100 (2016)CrossRefGoogle Scholar
  21. 21.
    Thai, T.H., Retraint, F., Cogranne, R.: Camera model identification based on the generalized noise model in natural images. Digit. Signal Proc. 48, 285–297 (2016)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Si, X., Jianjiang, F., Yuan, B., Zhou, J.: Dense registration of fingerprints. Pattern Recogn. 63, 87–101 (2017)CrossRefGoogle Scholar
  23. 23.
    Jain, A., Prasad, M.V.: A novel fingerprint indexing scheme using dynamic clustering. J. Reliab. Intell. Environ. 2(3), 59–171 (2016)CrossRefGoogle Scholar
  24. 24.
    Li, H.: Research on target information fusion identification algorithm in multi-sky-screen measurement system. IEEE Sens. J. 16(21), 7653–7658 (2016)CrossRefGoogle Scholar
  25. 25.
    Nair, S.A.H., Aruna, P.: Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems. Alex. Eng. J. 54(4), 1161–1174 (2015)CrossRefGoogle Scholar
  26. 26.
    Zhang, K., Huang, D., Zhang, B., Zhang, D.: Improving texture analysis performance in biometrics by adjusting image sharpness. Pattern Recogn. 66(1), 16–25 (2016)Google Scholar
  27. 27.
    Yijing, S., Jianjiang, F., Jie, Z.: Fingerprint indexing with pose constraint. Pattern Recogn. 54, 1–13 (2016)CrossRefGoogle Scholar
  28. 28.
    Swaminathan, M., Yadav, P.K., Piloto, O., Sjöblom, T., Cheong, I.: A new distance measure for non-identical data with application to image classification. Pattern Recogn. 63, 384–396 (2017)CrossRefGoogle Scholar
  29. 29.
    Wang, S., Deng, G., Hu, J.: A partial Hadamard transform approach to the design of cancelable fingerprint templates containing binary biometric representations. Pattern Recogn. 61, 447–458 (2017)CrossRefGoogle Scholar
  30. 30.
    Sasirekha, K., Thangavel, K.: A novel feature extraction algorithm from fingerprint image in wavelet domain. Comput. Intell. Cyber Secur. Comput. Models 1, 135–143 (2016)Google Scholar
  31. 31.
    Gupta, P., Gupta, P.: An accurate fingerprint orientation modeling algorithm. Appl. Math. Model. 40(15), 7182–7194 (2016)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Watson, C.I., Wilson, C.L.: NIST Special Database 4. Fingerprint Database. U.S. National Institute of Standards and Technology, Gaithersburg (1992)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSathyabama UniversityChennaiIndia
  2. 2.JEPPIAAR SRR Engineering CollegeChennaiIndia

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