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Combining Face and Iris Biometrics for Identity Verification

机译:结合面部和虹膜生物识别技术进行身份验证

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摘要

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. We use two different strategies 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. We compare the results of the combined classifier with the results of the individual face and iris classifiers.
机译:面部和虹膜识别已在各种生物识别应用中使用。除了提高验证性能外,这两种生物识别技术的融合还具有其他一些优势。我们使用两种不同的策略来融合虹膜和面部分类器。第一种策略是计算未加权或加权和,并将结果与​​阈值进行比较。第二种策略是将面部和虹膜分类器的匹配距离视为二维特征向量,并使用费舍尔判别分析和带有径向基函数的神经网络(RBFNN)等分类器将向量分类为真实或冒名顶替者。我们将组合分类器的结果与各个脸部和虹膜分类器的结果进行比较。

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