Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals

2023/06/05 by Puneet Kumar, Xiaobai Li, Kumar, Puneet +1
Psychology · #Artificial Intelligence (cs.AI) #Emotion and Mood Recognition #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2306.02845

openalex publication_date 2023/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper aims to demonstrate the importance and feasibility of fusing multimodal information for emotion recognition. It introduces a multimodal framework for emotion understanding by fusing the information from visual facial features and rPPG signals extracted from the input videos. An interpretability technique based on permutation feature importance analysis has also been implemented to compute the contributions of rPPG and visual modalities toward classifying a given input video into a particular emotion class. The experiments on IEMOCAP dataset demonstrate that the emotion classification performance improves by combining the complementary information from multiple modalities.

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