Paper
28 July 2023 Estimation of subtle facial emotion changes from expressionless images
Author Affiliations +
Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490H (2023) https://doi.org/10.1117/12.2690777
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France
Abstract
Subtle changes in emotional expressions occur more frequently compared to rich ones, which makes the evaluation of the emotional response of an individual challenging. In this study, we focus on the near-expressionless facial images, indicated with low arousal and valence value. We investigated the facial landmarks which are crucial in estimating subtle emotion through a novel feature selection method named Random Combination Selection with Iterative Step (RACSIS)1 . By combining appearance and geometrical features, while reducing the feature points up to 93.8%, the Mean Absolute Error (MAE) for Arousal = [-4 8], Valence = [-7 6], was reduced to 54.95% and 46.39% for the full emotional spectrum and the subtle emotion, respectively. We then tested the performance of the RACSIS to estimate the emotional response of participants undertaking audio-visual activities. We conclude that: 1. Appearance features played a greater role in reducing the MAE. 2. Feature selection (FS) by RACSIS achieved lower MAE values compared to correlation.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arvin Valderrama, Takumi Taketomi, Chandra Louis, Tamami Sanbongi, Akihiro Kuno, Satoru Takahashi, and Takeshi Nagata "Estimation of subtle facial emotion changes from expressionless images", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490H (28 July 2023); https://doi.org/10.1117/12.2690777
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KEYWORDS
Emotion

Education and training

Feature selection

Video

Medicine

Image analysis

Rain

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