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An efficient multimodal face recognition method robust to pose variation

机译:一种有效的多模式识别方法造成姿态变化

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In the recent years, face recognition has obtained much attention. Using combined 2D and 3D face recognition is an alternative method to deal with face recognition. A novel multimodal face recognition algorithm based on Gabor wavelet information is presented in this paper. The Principal Component Analysis (PCA) and the Linear Discriminant analysis (LDA) have been used for size reduction. The system has combined 2D and 3D systems in the decision level which presents higher performance in contrast with methods which use only 2D and 3D systems, separately. The proposed algorithm is examined with FRAV3D database that has faces with pose variation and 95% performance that is achieved in rank-one for fusion experiment.
机译:近年来,人脸识别获得了很多关注。 使用组合的2D和3D面部识别是处理人脸识别的替代方法。 本文介绍了一种基于Gabor小波信息的新型多模式识别算法。 主要成分分析(PCA)和线性判别分析(LDA)已被用于减少尺寸。 该系统在决策级别组合了2D和3D系统,其与仅使用仅2D和3D系统的方法具有相反的性能。 通过FRAV3D数据库检查所提出的算法,该数据库具有姿势变化的姿势和95%的性能,可以在融合实验中获得级别。

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