论文标题
直接分类情感强度
Direct Classification of Emotional Intensity
论文作者
论文摘要
在本文中，我们提出了一个模型，该模型可以直接从视频输入中预测情绪强度得分，而不是从动作单元中衍生。使用3D DNN与动态情感信息结合在一起，我们使用微笑的不同人的视频来训练模型，这些视频以0-10从0-10输出强度得分。每个视频都使用基于归一化动作单元的强度得分标记为框架。然后，我们的模型采用一种自适应学习技术来提高与新主题打交道时的性能。与其他模型相比，我们的模型在不同人之间的概括方面表现出色，并提供了一个新框架来直接对情感强度进行分类。
In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units. Using a 3d DNN incorporated with dynamic emotion information, we train a model using videos of different people smiling that outputs an intensity score from 0-10. Each video is labeled framewise using a normalized action-unit based intensity score. Our model then employs an adaptive learning technique to improve performance when dealing with new subjects. Compared to other models, our model excels in generalization between different people as well as provides a new framework to directly classify emotional intensity.
