Authentication Using Multimodal Biometric Features

Multimodal biometric systems is the consolidated multiple biometric sources, which enable the recognition performance better than the single biometric modality systems. The information fusion in a multimodal system can be performed at various levels like data level fusion, feature level fusion, match score level fusion and decision level fusion. In this paper, we have studied the performance of different fusion techniques and fusion rules in the context of a multimodal biometric system based on the finger print, hand geometry, knuckle extraction and speech traits of a user. Experiments showed that these fusion techniques showed a marked performance serial rule showed comparatively better performance.