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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >A NEW APPROACH FOR FACE-IRIS MULTIMODAL BIOMETRIC RECOGNITION USING SCORE FUSION
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A NEW APPROACH FOR FACE-IRIS MULTIMODAL BIOMETRIC RECOGNITION USING SCORE FUSION

机译：基于分数融合的人脸多模态生物识别的新方法

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摘要

In this paper, a new approach based on score level fusion is presented to obtain a robust recognition system by concatenating face and iris scores of several standard classifiers. The proposed method concatenates face and iris match scores instead of concatenating features as in feature-level fusion. The features from face and iris are extracted using local and global feature extraction methods such as PCA, subspace LDA, spPCA, mPCA and LBP. Transformation-based score fusion and classifier-based score fusion are then involved in the process to obtain, concatenate and classify the matching scores. Different fusion techniques at matching score level, feature level and decision level are compared with the proposed method to emphasize improvement and effectiveness of the proposed method. In order to validate the proposed scheme, a combined database is formed using ORL and BANCA face databases together with CASIA and UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed score level fusion achieves a significant improvement over unimodal methods and other multimodal face-iris fusion methods.
机译：在本文中，提出了一种基于分数水平融合的新方法，该方法通过将多个标准分类器的面部和虹膜分数进行串联来获得鲁棒的识别系统。所提出的方法将面部和虹膜匹配分数串联起来，而不是像在特征级融合中那样串联特征。使用局部和全局特征提取方法（例如PCA，子空间LDA，spPCA，mPCA和LBP）提取面部和虹膜的特征。然后，将基于转换的分数融合和基于分类器的分数融合纳入过程中，以获取，连接和分类匹配分数。将匹配分数级别，特征级别和决策级别的不同融合技术与所提出的方法进行比较，以强调所提出方法的改进和有效性。为了验证所提出的方案，使用ORL和BANCA人脸数据库以及CASIA和UBIRIS虹膜数据库形成了一个组合数据库。基于识别性能和ROC分析的结果表明，与单峰方法和其他多峰脸部虹膜融合方法相比，所提出的得分水平融合取得了显着改善。

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