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Title |
Password Hardened Multimodal Biometric Fuzzy Vault with Feature Points Extracted from Retina and Iris |
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Author |
V. S. Meenakshi, G. Padmavathi |
| Citation |
Vol. 9 No. 10 pp. 34-43 |
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Abstract |
Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person’s biometric is compromised his identity is lost. In contrast to password, biometric is not revocable. Therefore, providing security to the stored biometric template is very crucial. Crypto biometric systems are authentication systems, which blends the idea of cryptography and biometrics. Fuzzy vault is a proven crypto biometric construct which is used to secure the biometric templates. However fuzzy vault suffer from certain limitations like non-revocability, cross matching. Security of the fuzzy vault is affected by the non-uniform nature of the biometric data. Fuzzy vault when hardened with password overcomes these limitations. Password provides an additional layer of security and enhances user privacy. Retina has certain advantages over other biometric traits. Retinal scans are used in high-end security applications like access control to areas or rooms in military installations, power plants, and other high risk security areas. Multimodal biometric system performance is well compared to single modal biometric systems. The proposed multi modal biometric fuzzy vault includes combined feature points from retina and Iris. Iris and retinal capturing cameras can be combined as a single device to improve user convenience. The combined vault is hardened with user password for achieving high level of security. The security of the combined vault is measured using min-entropy. The proposed password hardened multi biometric fuzzy vault is robust towards stored biometric template attacks. |
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Keywords |
Biometric Template Security, Crypto Biometric Systems, Hardening Fuzzy Vault, Min-Entropy |
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