Multimodal Efficient Bioscrypt Authentication using MATLAB

Multimodal biometric systems are able to overcome some of these shortcomings, as mono-model biometric systems present a number of security issues and often offer unacceptable error rates. By combining two or more biometric systems into one identification system, multimodal biometrics improve the accuracy of authentication. However, the characteristics of a single biometric system should be statistically independent of the features of different biometrics systems. This article proposes a multimodal biometric system that can recognize fingerprints, faces and iris patterns. The system is applied to a point level that is consistent with different means of normalization and fusion. Compatibility scores are generated when query and database images are matched. The Fusion module combines the normalized and weighted sum scores to determine compatibility scores. The cumulative rule is used to combine these individual adjusted scores and their weights into a total score. Weights associated with each biometric attribute indicate how important that attribute is to the user, this system establishes an identity that is more trustworthy than individual biometric systems that establish identities by analyzing individual fingerprints. In a multimodal biometric system, multiple biometric properties are combined to enhance authentication performance and to reduce fraudulent access. The designed scheme exceeds single biometric systems in terms of reliability and accuracy.