Personalized Assessment of Arousal and Valence from Videos
| Autor: | Matthew Pediaditis, Maria Gabrani, Thomas Brunschwiler, Anca-Nicoleta Ciubotaru |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Speech recognition Deep learning 02 engineering and technology 010501 environmental sciences Sensor fusion Affect (psychology) 01 natural sciences Motion (physics) Arousal Behavioral modeling 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Emotion recognition Valence (psychology) business 0105 earth and related environmental sciences |
| Zdroj: | ICHI |
| DOI: | 10.1109/ichi48887.2020.9374354 |
| Popis: | Human behavior is influenced by numerous subjective factors such as the environment, culture, hormones, genes etc. This makes the development of a one-size-fits-all behavioral model for emotion recognition challenging, especially in the domain of affect recognition. In this paper we present a method to classify and assess arousal and valence from video in a personalized way. We represent the inherent information in the video independently through three semantically different types of signals, namely motion, appearance and physiology. We use a single- and multi-stream LSTM model for data fusion and classification, and compare our results against published values on a publicly available dataset consisting of 40 subjects. We further demonstrate that the personalized approach reaches better performance (Arousal: 78.16% avg. acc.; Valence 89.22% avg. acc.), while providing more insight into the role of each signal group. For arousal classification we can distinguish between subjects that show dominance of motion-related expressions against others that exhibit more static expressions. Fusion of all three signal types gave an advantage on very few subjects, a challenge that might be related to the video recordings being too short. |
| Databáze: | OpenAIRE |
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