Ontology type: schema:Chapter
2009
AUTHORSZhifang Wang , Qi Han , Xiamu Niu , Christoph Busch
ABSTRACTFeature-level fusion remains a challenging problem for multimodal biometrics. However, existing fusion schemes such as sum rule and weighted sum rule are inefficient in complicated condition. In this paper, we propose an efficient feature-level fusion algorithm for iris and face in parallel. The algorithm first normalizes the original features of iris and face using z-score model, and then take complex FDA as the classifier of unitary space. The proposed algorithm is tested using CASIA iris database and two face databases (ORL database and Yale database). Experimental results show the effectiveness of the proposed algorithm. More... »
PAGES356-364
Advances in Neural Networks – ISNN 2009
ISBN
978-3-642-01512-0
978-3-642-01513-7
http://scigraph.springernature.com/pub.10.1007/978-3-642-01513-7_38
DOIhttp://dx.doi.org/10.1007/978-3-642-01513-7_38
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