论文标题
使用情感数据集的基于上下文的情感识别
Context Based Emotion Recognition using EMOTIC Dataset
论文作者
论文摘要
在我们的日常生活和社交互动中，我们经常试图感知人们的情绪状态。在为机器提供相似的识别情绪的能力方面，已经进行了大量研究。从计算机视觉的角度来看，以前的大多数努力都集中在分析面部表情，在某些情况下也是身体姿势。这些方法中的一些在特定环境中非常有效。但是，它们的性能在自然，不受约束的环境中受到限制。心理研究表明，除了面部表情和身体姿势外，场景背景还为我们对人们情绪的看法提供了重要信息。但是，尚未深入探索自动情绪识别的上下文处理，部分原因是缺乏适当的数据。在本文中，我们介绍了情感，这是一个在各种自然情况下的人的图像的数据集，并以明显的情感注释。情感数据集结合了两种不同类型的情绪表示：（1）一组26个离散类别，以及（2）连续维度价，唤醒和优势。我们还提供了对数据集的详细统计和算法分析以及注释者的协议分析。使用情感数据集，我们训练不同的CNN模型以进行情感识别，将包含该人的边界框的信息与从场景中提取的上下文信息结合在一起。我们的结果表明，场景上下文如何提供重要信息，以自动识别情绪状态并激发进一步的研究。数据集和代码是开源的，可在以下网址提供：https：//github.com/rkosti/emotic and emotic and link for Peer-Reviewed发布的文章：https：//ieeexplore.ieee.org/document/8713881
In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacity of recognizing emotions. From a computer vision perspective, most of the previous efforts have been focusing in analyzing the facial expressions and, in some cases, also the body pose. Some of these methods work remarkably well in specific settings. However, their performance is limited in natural, unconstrained environments. Psychological studies show that the scene context, in addition to facial expression and body pose, provides important information to our perception of people's emotions. However, the processing of the context for automatic emotion recognition has not been explored in depth, partly due to the lack of proper data. In this paper we present EMOTIC, a dataset of images of people in a diverse set of natural situations, annotated with their apparent emotion. The EMOTIC dataset combines two different types of emotion representation: (1) a set of 26 discrete categories, and (2) the continuous dimensions Valence, Arousal, and Dominance. We also present a detailed statistical and algorithmic analysis of the dataset along with annotators' agreement analysis. Using the EMOTIC dataset we train different CNN models for emotion recognition, combining the information of the bounding box containing the person with the contextual information extracted from the scene. Our results show how scene context provides important information to automatically recognize emotional states and motivate further research in this direction. Dataset and code is open-sourced and available at: https://github.com/rkosti/emotic and link for the peer-reviewed published article: https://ieeexplore.ieee.org/document/8713881
