面部不对称量化用于表情不变人的识别

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI:10.1109/AFGR.2002.1004156

Yanxi Liu, Karen L. Schmidt, J. Cohn, S. Mitra

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引用次数: 154

摘要

我们研究了量化统计面部不对称作为一种生物特征在表情变化下的影响。我们的研究结果表明，面部不对称测量(AsymFaces)在计算上是可行的，包含了鉴别信息，并在两个公开可用的面部数据库(cohen -Kanade (T. Kanade等人，1999)和Feret (P.J. Phillips等人，1998))的图像数据上与Fisherface和Eigen-face方法相结合时提供协同作用。

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Facial asymmetry quantification for expression invariant human identification

We investigate the effect of quantified statistical facial asymmetry as a biometric under expression variations. Our findings show that the facial asymmetry measures (AsymFaces) are computationally feasible, containing discriminative information and providing synergy when combined with Fisherface and Eigen-face methods on image data of two publically available face databases (Cohn-Kanade (T. Kanade et al., 1999) and Feret (P.J. Phillips et al., 1998)).

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Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition

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