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An Investigation into Three Visual Characteristics of Complex Scenes that Evoke Human Emotion
Xin Lu1, Reginald B Adams1, Jia Li1
1The Pennsylvania State University, University Park, Pennsylvania, USA.
International Conference on Affective Computing and Intelligent Interaction and Workshops : [Proceedings]. ACII (Conference)
|October 8, 2019
Summary
New computational methods quantify visual characteristics like roundness and angularity to predict emotions. These new methods achieve accuracy comparable to hundreds of other visual features, enhancing emotion recognition in computer vision.
Area of Science:
- Computer Vision
- Psychology
- Affective Computing
Background:
- Previous studies analyzed hundreds of visual features (color, texture, composition) to predict emotional responses.
- Psychological studies identified roundness, angularity, and visual complexity as emotion-evoking features, but algorithmic capture proved challenging.
Purpose of the Study:
- To develop computational methods for quantifying roundness, angularity, and visual complexity in complex scenes.
- To compare the effectiveness of these new visual constructs against hundreds of previously examined features in predicting human emotions.
Main Methods:
- Generated a large dataset (EmoSet) of over 40,000 web images with human emotion ratings.
- Developed novel computational methods to derive indices for roundness, angularity, and complexity.
- Compared classification accuracy of new constructs with existing visual features.
Main Results:
- The new computational constructs for roundness, angularity, and complexity achieved classification accuracy comparable to hundreds of other visual features.
- Color features strongly correlated with the positivity of perceived emotions.
- Texture features related to calmness or excitement, while roundness, angularity, and complexity related to both emotion dimensions.
Conclusions:
- Roundness, angularity, and complexity are significant, computationally tractable predictors of human emotion.
- These new visual constructs offer a valuable addition to computational models of emotion recognition.
- The findings highlight the distinct roles of color, texture, and shape in shaping emotional perception.
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