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Implementing A Novel Biometric Cryptosystem using Similarity Distance Measure Function Focusing on the Quantization Stage


  • Department of Computer Science and Engineering, Sathyabama University, Chennai – 600119, Tamil Nadu, India
  • JEPPIAAR SRR Engineering College, Chennai - 603103, Tamil Nadu, India, India


Objectives: The essential requirement for a successful hashing method involves two distinct stages projection and quantization. In general, the projection stage is given much importance than the quantization stage. This stage has been concentrated in this paper which has equal importance as projection stage. Methods/Analysis: The using of Manhattan Distance method has been proposed in this paper instead of the widely used Hamming Distance, since it destroys the neighbourhood structure while measuring the similarity between points in the hashcode space. Findings: The problem of destroying the neighbourhood structure that existed in Hamming Distance is overcome by Manhattan hashing. Novelty/Improvement: The outperformance of Manhattan distance compared with Hamming distance has been shown and also, this paper has made an attempt to implement them in our Biocryptosystem to show its efficiency.


Biocryptosystem, Biometrics, Hamming Distance, Manhattan Hashing, Similarity Measure Functions.

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  • Mahalakshmi U, Sriram VS. An ECC based multibiometric system for enhancing security. Indian Journal of Science and Technology. 2013 Apr 1; 6(4):4299–305. DOI: 10.17485/ijst/2013/v6i4/31857.
  • Shah HNM, Ab Rashid MZ, Abdollah MF, Kamarudin MN, Chow KL, Kamis Z. Biometric voice recognition in security system. Indian Journal of Science and Technology. 2014; 7(2):104–12. DOI: 10.17485/ijst/2014/v7i2/47673.
  • Saminathan K, Chakravarthy T, Devi MC. Comparative study on biometric iris recognition based on hamming distance and multi block local binary pattern. Indian Journal of Science and Technology. 2015 Jun 1; 8(11):1. DOI: 10.17485/ijst/2015/v8i11/71764.
  • Market wired: Press Release and News Wire Services [Internet]. [Cited 2015 Jun 15]. Available from:
  • Yampolskiy RV, Govindaraju V. Similarity measure functions for strategy-based biometrics. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering. 2008 Dec 20; 2(12):4254–9.
  • Jin Z, Lim MH, Teoh AB, Goi BM. A non-invertible Randomized Graph-based Hamming embedding for generating cancellable fingerprint template. Pattern Recognition Letters. 2014(42):137–47.
  • Angadi SA, Gour S. Euclidean distance based offline signature recognition system using global and local wavelet features. 2014 Fifth International Conference on Signal and Image Processing (ICSIP); 2014 Jan 8. p. 87–91.
  • Belguechi R, Lacharme P, Rosenberger C. Enhancing the privacy of electronic passports. International Journal of Information Technology and Management. 2012 Jan 1; 11(1–2):122–37.
  • Andoni A, Indyk P. Near-optimal hashing algorithms for approximate nearest neighbour in high dimensions. 47th Annual IEEE Symposium on Foundations of Computer Science, 2006. FOCS’06; 2006 Oct. p. 459–68.
  • Gionis A, Indyk P, Motwani R. Similarity search in high dimensions via hashing. VLDB ‘99 Proceedings of the 25th International Conference on Very Large Data Bases; 1999. p. 518–29.
  • Datar M, Immorlica N, Indyk P, Mirrokni VS. Locality-sensitive hashing scheme based on p-stable distributions. Proceedings of the Twentieth Annual Symposium on Computational Geometry; 2004 Jun 8. p. 253–62.
  • Kulis B, Grauman K. Kernelized locality-sensitive hashing for scalable image search. 2009 IEEE 12th International Conference on Computer Vision; 2009 Sep 29. p. 2130–37.
  • Kulis B, Jain P, Grauman K. Fast similarity search for learned metrics. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009 Dec; 31(12):2143–57.
  • Mu Y, Yan S. Non-metric locality-sensitive hashing. AAAI; 2010 Jul 3.
  • Lee K, Park H. A new similarity measure based on intraclass statistics for biometric systems. ETRI Journal. 2003 Oct 14; 25(5):401–6.
  • Sturn A. Cluster analysis for large scale gene expression studies (Doctoral dissertation, Graz University of Technology).
  • Yampolskiy RV, Govindaraju V. Use of behavioral biometrics in intrusion detection and online gaming. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series; 2006 Apr.
  • Mahalanobis distance [Internet]. [Cited 2015 Jul 25]. Available from:
  • Gong Y, Lazebnik S. Iterative quantization: a procrustean approach to learning binary codes. 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2011 Jun 20. p. 817–24.
  • Raginsky M, Lazebnik S. Locality-sensitive binary codes from shift-invariant kernels. Advances in Neural Information Processing Systems; 2009. p. 1509–17.
  • Xu H, Wang J, Li Z, Xeng G, Li S, Yu N. Complementary hashing for approximate nearest neighbour search. 2011 IEEE International Conference on Computer Vision (ICVC); 2011 Nov 6. p. 1631–38.
  • Liu W, Wang J, Kumar S, Chang SF. Hashing with graphs. Proceedings of the 28th International Conference on Machine Learning (ICML-11); 2011. p. 1–8.
  • Zhen Y, Yeung DY. Active hashing and its application to image and text retrieval. Data Mining and Knowledge Discovery. 2013 Mar 1; 26(2):255–74.
  • Zhen Y, Yeung DY. A probabilistic model for multimodal hash function learning. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2012 Aug 12. p. 940–48.
  • Norouzi MM, Blei DM. Minimal loss hashing for compact binary codes. Proceedings of the 28th International Conference on Machine Learning (ICML-11); 2011. p. 353–60.
  • Zhang D, Wang F, Si LA. Composite hashing with multiple information sources. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval; 2011 Jul 24. p. 225–34.
  • Kong W, Li WJ, Guo M. Manhattan hashing for large-scale image retrieval. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval; 2012 Aug 12. p. 45–54.


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