Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition

TL;DR

Milmer is a multimodal emotion recognition framework that combines facial expressions and EEG signals using transformer-based fusion and multiple instance learning to improve accuracy in human-computer interaction applications.

Contribution

This work introduces a novel multimodal framework with a transformer-based fusion and MIL approach for emotion recognition, enhancing feature extraction and temporal dynamics modeling.

Findings

Achieved 96.72% accuracy on DEAP dataset

Validated effectiveness of each module through ablation studies

Outperformed existing methods in multimodal emotion recognition

Abstract

Emotions play a crucial role in human behavior and decision-making, making emotion recognition a key area of interest in human-computer interaction (HCI). This study addresses the challenges of emotion recognition by integrating facial expression analysis with electroencephalogram (EEG) signals, introducing a novel multimodal framework-Milmer. The proposed framework employs a transformer-based fusion approach to effectively integrate visual and physiological modalities. It consists of an EEG preprocessing module, a facial feature extraction and balancing module, and a cross-modal fusion module. To enhance visual feature extraction, we fine-tune a pre-trained Swin Transformer on emotion-related datasets. Additionally, a cross-attention mechanism is introduced to balance token representation across modalities, ensuring effective feature integration. A key innovation of this work is the adoption of a multiple instance learning (MIL) approach, which extracts meaningful information from multiple facial expression images over time, capturing critical temporal dynamics often overlooked in previous studies. Extensive experiments conducted on the DEAP dataset demonstrate the superiority of the proposed framework, achieving a classification accuracy of 96.72% in the four-class emotion recognition task. Ablation studies further validate the contributions of each module, highlighting the significance of advanced feature extraction and fusion strategies in enhancing emotion recognition performance. Our code are available at https://github.com/liangyubuaa/Milmer.