Correlation between Human Emotion and Temporal·Spatial Contexts by Analyzing Environmental Factors

Abstract

In this paper, we propose a method for extracting emotional factors through audiovisual quantitative feature analysis from images of the surrounding environment. Nine features were extracted such as time complexity, spatial complexity (horizontal and vertical), color components (hue and saturation), intensity, contrast, sound amplitude, and sound frequency. These nine features were used to infer “pleasant-unpleasant” and “arousal-relaxation” scores through two support vector regressions. First, the inference accuracy for each of the nine features was calculated as a hit ratio to check the distinguishing power of the features. Next, the difference between the position in the two-dimensional emotional plane inferred through SVR and the ground truth determined subjectively by the subject was examined. As a result of the experiment, it was confirmed that the time-complexity feature had the best classification performance, and it was confirmed that the emotion inferred through SVR can be valid when the two-dimensional emotional plane is divided into 3 × 3.