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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Multi-modality Network with Visual and Geometrical Information for Micro Emotion Recognition
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Multi-modality Network with Visual and Geometrical Information for Micro Emotion Recognition

机译：具有微观情感识别的视觉和几何信息的多模态网络

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摘要

Micro emotion recognition is a very challenging problem because of the subtle appearance variants among different facial expression classes. To deal with the mentioned problem, we proposed a multi-modality convolutional neural networks (CNNs) based on visual and geometrical information in this paper. The visual face image and structured geometry are embedded into a unified network and the recognition accuracy can be benefic from the fused information. The proposed network includes two branches. The first branch is used to extract visual feature from color face images, and another branch is used to extract the geometry feature from 68 facial landmarks. Then, both visual and geometry features are concatenated into a long vector. Finally, the concatenated vector is fed to the hinge loss layer. Compared with the CNN architecture only used face images, our method is more effective and has got better performance. In the final testing phase of Micro Emotion Challenge~1, our method has got the first place with the misclassification of 80.212137.
机译：微观情绪识别是一个非常具有挑战性的问题，因为不同的面部表情课程中的细微外观变体。为了处理所提到的问题，我们在本文中提出了一种基于视觉和几何信息的多模式卷积神经网络（CNNS）。视觉面部图像和结构化几何形状嵌入到统一网络中，并且识别精度可能是融合信息的受益。所提出的网络包括两个分支。第一分支用于从彩色面部图像中提取视觉特征，另一个分支用于从68个面部地标中提取几何特征。然后，视觉和几何特征均连接到长向量中。最后，将级联载体送入铰链损耗层。与CNN架构仅使用脸部图像相比，我们的方法更有效并且具有更好的性能。在微观情绪挑战的最终测试阶段〜1中，我们的方法已经获得了80.212137的错误分类。

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