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Context Based Emotion Recognition using EMOTIC Dataset.

08:00 EDT 14th May 2019 | BioPortfolio

Summary of "Context Based Emotion Recognition using EMOTIC Dataset."

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, contributes 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 natural and different situations annotated with their apparent emotion. The EMOTIC database 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 database we train different CNN models for emotion recognition, combining the information of the person bounding box with the information present in the scene context. Our results show how scene context contributes important information to automatically recognize emotional states and motivate further research in this direction.

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This article was published in the following journal.

Name: IEEE transactions on pattern analysis and machine intelligence
ISSN: 1939-3539
Pages:

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