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


Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Chennai – 600119, Tamil Nadu, India
2 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.

Keywords

Biocryptosystem, Biometrics, Hamming Distance, Manhattan Hashing, Similarity Measure Functions.
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  • Implementing A Novel Biometric Cryptosystem using Similarity Distance Measure Function Focusing on the Quantization Stage

Abstract Views: 153  |  PDF Views: 0

Authors

R. Ramya
Department of Computer Science and Engineering, Sathyabama University, Chennai – 600119, Tamil Nadu, India
T. Sasikala
JEPPIAAR SRR Engineering College, Chennai - 603103, Tamil Nadu, India, India

Abstract


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.

Keywords


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



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i22%2F134403