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