Cloud computing is mainly used for storage service. The security issues such as privacy breach, malicious modification and loss of data make it more vulnerable whereas fog computing is a recent research tool which is trending to bring cloud computing services to network edges. Fog computing is been used as a storage employment in multiple clouds. To make the cloud storage more reliable, in this paper we have used AES and Base64 algorithm, where AES algorithm is faster and can encrypt data in blocks that is also suitable for image encryption. Base64 algorithm converts the binary encrypted data to a string. Many studies have proved the AES encryption is most secure method for data encryption. Also, we have implemented a special feature of splitting data and storing it on distinct cloud severs. This makes the proposed scheme more reliable for storage of data.
Biometric system is a very important recognition system which is used for individual verification and identification. Various types of biometric traits are used in today's world, in which some are used for commercial purpose and few used for verification purpose. Existing authentication techniques are suffer from different errors like mismatch image, spoofing, falsification in the data, to solve this errors the combination of Electrocardiography(ECG) and fingerprint multimodal is introduced. This proposed modal produces effective recognition system when compared to individual recognition system. The proposed multimodal recognition system provides optimum results compared to the individual recognition system which yields better results for authentication compared to the Existing system.
Abstract-An increased demand of biometric authentication coupled with automation of systems is observed in the recent times. Generally biometric recognition systems currently used consider only a single biometric characteristic for verification or authentication. Researchers have proved the inefficiencies in unimodal biometric systems and propagated the adoption of multimodal biometric systems for verification. This paper introduces Bi-modal Biometric Verification Mechanism using Fingerprint and Face (BBVMFF). The BBVMFF considers the frontal face and fingerprint biometric characteristics of users for verification. The BBVMFF Considers both the Gabor phase and magnitude features as biometric trait definitions and simple lightweight feature level fusion algorithm. The fusion algorithm proposed enables the applicability of the proposed BBVMFF in unimodal and Bi-modal modes proved by the experimental results presented.
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