Facial expression decomposition
Facial expression decomposition
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DOI：
10.1109/iccv.2003.1238452
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发表时间：
2003-10
期刊：
影响因子：
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通讯作者：
Hongcheng Wang;N. Ahuja
中科院分区：
文献类型：
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作者：
Hongcheng Wang;N. Ahuja
在本文中，我们提出了一种新的面部表情分解方法-高阶奇异值分解（HOSVD），矩阵SVD的自然推广。我们学习的表情子空间和人的子空间，从一个语料库的图像显示七个基本的面部表情，而不是诉诸专家编码的面部表情参数。我们提出了一种同时人脸和面部表情识别算法，它可以将给定的图像分类到七个基本的面部表情类别之一，然后可以使用学习的表情子空间模型合成新人的其他面部表情。本文的贡献主要体现在两个方面。首先，我们提出了一个新的HOSVD为基础的方法来模拟人与表情之间的映射，用于面部表情合成一个新的人。其次，我们实现了同时人脸和面部表情识别作为面部表情分解的结果。实验结果表明，在合成和识别任务的人的子空间和表情子空间的能力。作为合成质量的定量度量，我们提出使用梯度最小平方误差（GMSE）来度量原始图像和合成图像之间的梯度差。
In this paper, we propose a novel approach for facial expression decomposition - higher-order singular value decomposition (HOSVD), a natural generalization of matrix SVD. We learn the expression subspace and person subspace from a corpus of images showing seven basic facial expressions, rather than resort to expert-coded facial expression parameters. We propose a simultaneous face and facial expression recognition algorithm, which can classify the given image into one of the seven basic facial expression categories, and then other facial expressions of the new person can be synthesized using the learned expression subspace model. The contributions of this work lie mainly in two aspects. First, we propose a new HOSVD based approach to model the mapping between persons and expressions, used for facial expression synthesis for a new person. Second, we realize simultaneous face and facial expression recognition as a result of facial expression decomposition. Experimental results are presented that illustrate the capability of the person subspace and expression subspace in both synthesis and recognition tasks. As a quantitative measure of the quality of synthesis, we propose using gradient minimum square error (GMSE) which measures the gradient difference between the original and synthesized images.