Efficient Multimodal Biometric Feature Fusion Using Block Sum and Minutiae Techniques

Biometric is widely used for identifying a person in different area like Security zones, Border crossings, Airports, Automatic teller machines, Passport, Criminal verification, etc. Currently, most of the deployed biometric systems use a single biometric trait for recognition. But there are several limitations of unimodal biometric system, such as Noise in sensed data, Non-universality, higher error rate, and lower recognition rate. These issues can be handled by designing a Multimodal biometric system. This research paper proposes a novel feature level fusion technique based on a distance metric to improve both recognition rate and response time. This algorithm is based on the textural features extracted from iris using Block sum and fingerprint using Minutiae method. The performance of the propose algorithms has been validated and compared with the other algorithms using the CASIA Version 3 iris database and YCCE Fingerprint database.

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