AES Based Multimodal Biometric Authentication using Cryptographic Level Fusion with Fingerprint and Finger Knuckle Print
Muthukumar Arunachalam1 and Kannan Subramanian2
1Department of Electronics and Communication Engineering, Kalasalingam University, Krishnankoil
2Department of Electrical and Electronics Engineering, Kalasalingam University, Krishnankoil
Abstract: In general, the identification and verification are done by passwords, pin number, etc., which are easily cracked by others. In order to, overcome this issue biometrics is a unique tool to authenticate an individual person. Biometric is a quantity which consists of an individual physical characteristics of Fingerprint, Finger Knuckle Print (FKP), Iris, Face and so on. These characteristics are not easily cracked by others. Nevertheless, unimodal biometric suffers due to noise, intra class variations, spoof attacks, non-universality and some other attacks. In order to avoid these attacks, the multimodal biometrics i.e. a combination of more modalities is adapted. They are combined with cryptography, which will give more security for physical characters of biometrics. Bio-crypto system provides the authentication as well as the confidentiality of the data. This paper proposes to improve the security of multimodal systems by generating the biometric key from Fingerprint and FKP biometrics with its feature extraction using K-Means algorithm. The secret value is encrypted with biometric key using Symmetric Advanced Encryption Standard (AES) Algorithm. This paper also discusses about the integration of Fingerprint and FKP using package model cryptographic level fusion in order to improve the overall performance of the system. The encryption process will give more authentication and security for the system. The Cyclic Redundancy Check (CRC) function protects the biometric data from malicious tampering, and also it provides error checking functionality.
Keywords: AES algorithm, Biometric crypto-systems, CRC, Cryptographic level fusion methodology, K-Means algorithm, Multimodal biometrics.
Received May 17, 2013; accepted September 19, 2013