Using Scene and Semantic Features for Multi-modal Emotion Recognition

• URL: http://arxiv.org/abs/2308.00228v1
• Date: Tue, 1 Aug 2023 01:54:55 GMT
• Title: Using Scene and Semantic Features for Multi-modal Emotion Recognition
• Authors: Zhifeng Wang and Ramesh Sankaranarayana
• Abstract summary: We propose to use combined scene and semantic features, along with personal features, for multi-modal emotion recognition. We use a modified EmbraceNet to extract features from the images, which is trained to learn both the body and pose features simultaneously. We report an average precision of 40.39% across the 26 emotion categories, which is a 5% improvement over previous approaches.
• Score: 1.0152838128195467
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic emotion recognition is a hot topic with a wide range of applications. Much work has been done in the area of automatic emotion recognition in recent years. The focus has been mainly on using the characteristics of a person such as speech, facial expression and pose for this purpose. However, the processing of scene and semantic features for emotion recognition has had limited exploration. In this paper, we propose to use combined scene and semantic features, along with personal features, for multi-modal emotion recognition. Scene features will describe the environment or context in which the target person is operating. The semantic feature can include objects that are present in the environment, as well as their attributes and relationships with the target person. In addition, we use a modified EmbraceNet to extract features from the images, which is trained to learn both the body and pose features simultaneously. By fusing both body and pose features, the EmbraceNet can improve the accuracy and robustness of the model, particularly when dealing with partially missing data. This is because having both body and pose features provides a more complete representation of the subject in the images, which can help the model to make more accurate predictions even when some parts of body are missing. We demonstrate the efficiency of our method on the benchmark EMOTIC dataset. We report an average precision of 40.39\% across the 26 emotion categories, which is a 5\% improvement over previous approaches.

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