Combining Face and Iris Biometrics for Identity Verification

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Date:

16-04-2005
Vendor:

Michigan State University
Registration:

Required
Type:

Whitepapers
File size:

421.4 KB
Format:

PDF, Acrobat Reader
Pages:

8

Publisher’s description

Face and iris identification have been employed in various biometric applications. Besides improving verification performance, the fusion of these two biometrics has several other advantages. Two different strategies have been used for fusing iris and face classifiers. The first strategy is to compute either an unweighted or weighted sum and to compare the result to a threshold. The second strategy is to treat the matching distances of face and iris classifiers as a two-dimensional feature vector and to use a classifier such as Fisher's discriminant analysis and a neural network with radial basis function (RBFNN) to classify the vector as being genuine or an impostor. The results of the combined classifier are compared with the results of the individual face and iris classifiers in this paper.

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Combining Face and Iris Biometrics for Identity Verification