In this paper we describe our approach to the arousal and valence track of the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). We extracted facial features using OpenFace and used them to train a multiple output random forest regressor. Our approach performed comparable to the baseline approach on valence and outperformed in on arousal.
<p>Modeling of human emotion is a challenging problem that can require multiple signals types, as well as contextual information that has been obtained over time. Considering this, in this paper we present our approach, based on physiological signals, to the Emotion Physiology and Experience Collaboration (EPIC) challenge at Affective Computing & Intelligent Interaction (ACII), 2023. In total there are four scenarios that we model: 1) across time; 2) across subjects; 3) across elicitor; and 4) across version. To tackle this challenge, we propose to use a physiological fusion-based approach to solve each scenario. Along with this, we give a detailed analysis of the evaluated physiological signals and personalized predictions for each subject are shown. Our proposed approach shows encouraging results with the lowest root mean square error achieved for scenario 4 (across version) for both valence and arousal on the challenge test set.</p>
<p>Modeling of human emotion is a challenging problem that can require multiple signals types, as well as contextual information that has been obtained over time. Considering this, in this paper we present our approach, based on physiological signals, to the Emotion Physiology and Experience Collaboration (EPIC) challenge at Affective Computing & Intelligent Interaction (ACII), 2023. In total there are four scenarios that we model: 1) across time; 2) across subjects; 3) across elicitor; and 4) across version. To tackle this challenge, we propose to use a physiological fusion-based approach to solve each scenario. Along with this, we give a detailed analysis of the evaluated physiological signals and personalized predictions for each subject are shown. Our proposed approach shows encouraging results with the lowest root mean square error achieved for scenario 4 (across version) for both valence and arousal on the challenge test set.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.