Facial Expression Recognition by De-expression Residue Learning
Facial Expression Recognition by De-expression Residue Learning
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DOI：
10.1109/cvpr.2018.00231
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Huiyuan Yang;U. Ciftci;L. Yin
中科院分区：
文献类型：
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作者：
Huiyuan Yang;U. Ciftci;L. Yin
面部表情是一个人的表达成分和中性成分的结合。在本文中，我们提出通过一种称为“去表达残余学习”（DeRL）的去表达学习过程提取表达成分的信息来识别面部表情。首先，利用cGAN对生成模型进行训练。该模型对任意输入的人脸图像生成相应的中性人脸图像。我们称这个过程为去表达，因为表达性信息被生成模型过滤掉了；然而，表达性信息仍然记录在中间层。对于中性的人脸图像，与之前使用像素级或特征级差异进行面部表情分类的工作不同，我们的新方法学习了保留在生成模型中间层中的沉积（或残留物）。这样的残差是必不可少的，因为它包含了从任何输入的面部表情图像沉积在生成模型中的表达成分。我们的实验使用了7个公共面部表情数据库。使用两个数据库（BU-4DFE和BP4D-spontaneous）进行预训练，在CK+、Oulu-CASIA、MMI、BU-3DFE和BP4D+ 5个数据库上对DeRL方法进行了评估。实验结果证明了该方法的优越性。
A facial expression is a combination of an expressive component and a neutral component of a person. In this paper, we propose to recognize facial expressions by extracting information of the expressive component through a de-expression learning procedure, called De-expression Residue Learning (DeRL). First, a generative model is trained by cGAN. This model generates the corresponding neutral face image for any input face image. We call this procedure de-expression because the expressive information is filtered out by the generative model; however, the expressive information is still recorded in the intermediate layers. Given the neutral face image, unlike previous works using pixel-level or feature-level difference for facial expression classification, our new method learns the deposition (or residue) that remains in the intermediate layers of the generative model. Such a residue is essential as it contains the expressive component deposited in the generative model from any input facial expression images. Seven public facial expression databases are employed in our experiments. With two databases (BU-4DFE and BP4D-spontaneous) for pre-training, the DeRL method has been evaluated on five databases, CK+, Oulu-CASIA, MMI, BU-3DFE, and BP4D+. The experimental results demonstrate the superior performance of the proposed method.