Biometric is the process which is used to measure people’s distinctive physical and behavioural characteristics with the help of mathematical analysis. The technology is principally used for detection and right to use management, or for distinguishing people WHO area unit beneath police work. Now a days used biometric systems are of face, fingerprints, iris, retina, signature, palm print, identification and so on to see a person’s identity. In this paper, we have a tendency to contemplate face and fingerprint features for authentication and confirmation. Victimisation this knowledge we have a tendency to project a model for authentication in multimodal biometry that is typically referred to as Context-Sensitive Exponent Associative Memory Model (CSEAM). CSEAM applied on biometry patterns and afford security for the data. In the first step of this paper, Discrete Wavelet Transformation (DWT) face and finger can be applied at first and then Fusion can be applied . In the second stage Principle Component Analysis (PCA) can be applied at first and then Singular Value Decomposition (SVD) can be applied to extract features. In the third stage these features can be stored for authentication and verification in smart cards). In CSEAM model, exponential Kronecker product applied for verification and authentication on input samples. Verification and authentication can be done using different key sizes. This paper shows better results for the key size of 8x8 by using DWT while comparing to the Pavan Kumar K[1] et all.

Biometric, Discrete Wavelet Transformation, exponential kronecker product

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