The biometric authentication is an efficient alternative for conventional authentication techniques. Researches in this field show that multi-model biometric systems perform better than single mode. The basic idea of multi-model biometrics is the integration (fusion) of the various biometric mode data. Information from multiple sources can be integrated at three distinct levels: (i) feature extraction level; (ii) match score level; and (iii) decision level. Fusions at the match score and decision levels have been studied extensively by researchers where as fusion at the feature level is a relatively understudied problem. In this paper, we present a reinvigorated technique of feature-based fusion in a special kind of multimodal system where multiple fingerprints are used. The results from the analysis of previous works indicate that the proposed technique can lead to substantial improvement in multimodal matching performance

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Correlation Feature level fusion Multi-model biometric system Multiple fingerprints