Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes
Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes
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DOI：
10.2197/ipsjtcva.1.231
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Xu Qiao;R. Xu;Yenwei Chen;T. Igarashi;K. Nakao;A. Kashimoto
中科院分区：
文献类型：
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作者：
Xu Qiao;R. Xu;Yenwei Chen;T. Igarashi;K. Nakao;A. Kashimoto
提出了一种基于广义N维主成分分析（GND-PCA）的人脸图像统计外观建模方法，该方法适用于不同人、不同视点、不同光照等多种模式下的人脸图像统计外观建模。具有多种模式的人脸图像可以被认为是高维数据。GND-PCA可以更有效地表示高阶维数据。我们进行了广泛的实验上的MaVIC数据库（KAO-Ritsumeikan多角度视图，照明和化妆品面部数据库），以评估所提出的算法的有效性，并比较了传统的ND-PCA的重建误差。结果表明，GND-PCA比PCA和ND-PCA更有效地提取数据特征。
This paper introduces a framework called generalized N-dimensional principal component analysis (GND-PCA) for statistical appearance modeling of facial images with multiple modes including different people, different viewpoint and different illumination. The facial images with multiple modes can be considered as high-dimensional data. GND-PCA can represent the high-order dimensional data more efficiently. We conduct extensive experiments on MaVIC Database (KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database) to evaluate the effectiveness of the proposed algorithm and compared the conventional ND-PCA in terms of reconstruction error. The results indicated that the extraction of data features is computationally more efficient using GND-PCA than PCA and ND-PCA.