Feature-Level Fusion of Iris and Face for Personal Identification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

Zhifang Wang , Qi Han , Xiamu Niu , Christoph Busch

ABSTRACT

Feature-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... »

PAGES

356-364

Book

TITLE

Advances in Neural Networks – ISNN 2009

ISBN

978-3-642-01512-0
978-3-642-01513-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-01513-7_38

DOI

http://dx.doi.org/10.1007/978-3-642-01513-7_38

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033936427


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