Multi-modal Transfer Learning for Dynamic Facial Emotion Recognition in the Wild

• URL: http://arxiv.org/abs/2504.21248v1
• Date: Wed, 30 Apr 2025 01:09:11 GMT
• Title: Multi-modal Transfer Learning for Dynamic Facial Emotion Recognition in the Wild
• Authors: Ezra Engel, Lishan Li, Chris Hudy, Robert Schleusner,
• Abstract summary: Facial expression recognition (FER) is a subset of computer vision with important applications for human-computer-interaction, healthcare, and customer service.<n>In this paper, we examine the use of multi-modal transfer learning to improve performance on a challenging video-based FER dataset.
• Score: 0.14999444543328289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial expression recognition (FER) is a subset of computer vision with important applications for human-computer-interaction, healthcare, and customer service. FER represents a challenging problem-space because accurate classification requires a model to differentiate between subtle changes in facial features. In this paper, we examine the use of multi-modal transfer learning to improve performance on a challenging video-based FER dataset, Dynamic Facial Expression in-the-Wild (DFEW). Using a combination of pretrained ResNets, OpenPose, and OmniVec networks, we explore the impact of cross-temporal, multi-modal features on classification accuracy. Ultimately, we find that these finely-tuned multi-modal feature generators modestly improve accuracy of our transformer-based classification model.

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