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Spontaneous Emotion Recognition in Response to Videos

机译:视频自发情感识别

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In order to understand human emotions correctly taking into account only facial expressions, we are conducting the experiments on the spontaneous emotional facial videos of people watching musical video clips from DEAP open source dataset. We are reporting the comparative results of emotion recognition done in two ways: sequential extraction of spatial and temporal features done by CNN-RNN, simultaneous extraction of both types of features performed by 3D convolutions in our C3D networks architecture. In order to study the contribution of microex-pressions to emotion recognition we are augmenting videos in two ways: reducing to 1 fps, thus losing a significant amount of temporal information, reducing to 10 fps, thus preserving most of the muscle movement information.
机译:为了仅考虑面部表情就正确理解人的情绪,我们正在对人们观看DEAP开源数据集的音乐视频剪辑的自发性情绪面部视频进行实验。我们正在报告通过两种方式完成的情感识别的比较结果:依次提取CNN-RNN完成的时空特征,同时提取C3D网络架构中3D卷积执行的两种类型的特征。为了研究微表情对情绪识别的贡献,我们以两种方式增强视频:降低至1 fps,从而丢失了大量的时间信息,降低至10 fps,从而保留大多数肌肉运动信息。

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