Multi-Modality Network With Visual And Geometrical Information For Micro Emotion Recognition
2017 12TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2017)(2017)
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.
更多查看译文
关键词
microemotion recognition,multimodality network,visual information,geometrical information,facial expression classes,multimodality convolutional neural networks,visual face image,structured geometry,visual feature extraction,facial landmarks,geometry feature extraction,CNN architecture,color face images
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络