Cryptographic Algorithm based Feature Level Fusion of Fingerprint and Iris in a Multi-Biometric Recognition System
Jayapriya1, Umamaheswari K2
1Jayapriya , Department of Information Technology, PSG College of Technology, Anna University, India
2Umamaheswari K, Department of Information Technology, PSG College of Technology, Anna university, India
Manuscript received on November 15, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 2354-2360 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6542129219/2019©BEIESP | DOI: 10.35940/ijitee.B6542.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Biometric encryption is one of the developing exploration area, which is a strategy for merging biometric features with cryptographic keys. Biometric Recognition is based on the anatomical and behavior attributes of the individuals. Multibiometric is the combination of various biometrics like Fingerprint, Iris, and Face, Fingervein etc. Experts are concentrating on the most proficient method to give security to the framework, the template which was produced from the biometric should be ensured. The main objective of this paper is to protect the multi biometric template by creating a protected sketch by deploying bio cryptosystem. Once the biometric template is stolen it turns into a major issue for the security of the framework and furthermore for client protection. In this way, a bio-crypto framework ensures the confidentiality of the information. In this paper bio cryptosystem is proposed to improve the security of multimodal frameworks by producing the biocrypto key from Finger print and iris. Gray level co-occurrence matrix (GLCM) based Haralick features, local binary pattern (LBP), triplet half-band filter bank (THFB) and dynamic features (DF) are extracted from fingerprint and iris. The high dimensionality space of the features are reduced using kernel principal component analysis (KPCA. Finally, the encoding process is matted with biometric key utilizing symmetric RSA (Rivest-Shamir-Adleman) cryptographic algorithm.
Keywords: Multi Biometric, Recognition System, KPCA, RSA, Fusion, Feature Extraction.
Scope of the Article: Pattern Recognition