An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition
Byung Hyung Kim, Sungho Jo

TL;DR
This paper introduces A-Situ, a computational framework that uses physiological behaviors and affective curves to reliably label emotions in real-world situations, bridging cognitive and affective perception.
Contribution
The novel framework models affective situations with affective curves derived from physiological data, improving emotion labeling accuracy in real-life contexts.
Findings
Affective curves effectively discriminate emotional states.
Physiological data correlates with continuous emotion labels.
System outperforms existing explicit emotion assessments.
Abstract
This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as a set of labels in the valence-arousal emotion space. Based on physiological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatiotemporal situation is represented as a polynomial curve called the affective curve, which bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve for reliable emotion labeling in real-world experiments, respectively concerning 1) a comparison between the results from our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEmotion and Mood Recognition · EEG and Brain-Computer Interfaces · Neural dynamics and brain function
