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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >Multimodal 2D and 3D for In-The-Wild Facial Expression Recognition
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Multimodal 2D and 3D for In-The-Wild Facial Expression Recognition

机译：用于野生面部表情识别的多峰2D和3D

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摘要

In this paper, unlike other in-the-wild facial expression recognition (FER) studies which only focused on 2D information, we present a fusion approach for 2D and 3D facial data in FER. In particular, the 3D facial data are first reconstructed from image datasets. The 3D information are then extracted by deep learning technique that could exploit the meaningful facial geometry details for expression. We further demonstrate the potential of using 3D facial data by taking the 2D projected images of 3D face as an additional input for FER. These features are fused with that of 2D features from a typical network. Following the experiment procedure in recent studies, the concatenated features are classified by linear support vector machines (SVMs). Comprehensive experiments are further conducted on integrating facial features for expression prediction. The results show that the proposed method achieves state-of-the-art recognition performances on both RAF database and SFEW 2.0 database. This is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.
机译：在本文中，与其他仅关注2D信息的其他内容表达识别（FER）研究，我们在FER中展示了2D和3D面部数据的融合方法。特别地，首先从图像数据集重建3D面部数据。然后通过深度学习技术提取3D信息，该技术可以利用用于表达的有意义的面部几何细节。我们进一步展示了通过将3D投影图像作为FER的额外输入来使用3D面部数据来使用3D面部数据的潜力。这些功能与典型网络的2D特征融合。在近期研究中的实验程序之后，级联特征由线性支持向量机（SVM）分类。进一步对整合面部特征进行表达预测的综合实验。结果表明，该方法在RAF数据库和SFew 2.0数据库上实现了最先进的识别性能。这是第一次在野外焦点的背景下呈现了3D和2D面部方式的深层学习组合。

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