LAUSR

In recent days, due to the advent of advanced technologies such as cloud computing, accessing data can be done anywhere at any time. Meanwhile, ensuring the data security is highly significant. Authentication plays a major role in preserving security via different access control mechanisms. As a recent trend, the biological information of the individual user is considered as verification scheme for the authentication process. Traits such as fingerprint, iris, ear or palm print are widely used to develop the authentication systems from its patterns. But, to increase the complexity of the user authentication and to ensure high security, more than a trait is combined together. In this paper, a multimodal authentication system is proposed by fusing the feature points of fingerprint, iris and palm print traits. Each trait has undergone the following procedures of image processing techniques such as pre-processing, normalization and feature extraction. From the extracted features, a unique secret key is generated by fusing the traits in two stages. False Acceptance Rate (FAR) and False Rejection Rate (FRR) metrics are used to measure the robustness of the system. This performance of the model is evaluated using three standard symmetric cryptographic algorithms such as AES, DES and Blowfish. This proposed model provides better security and access control over data in cloud environment.