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arXiv · 2504.21248

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

Ezra Engel·Lishan Li·Chris Hudy·Robert Schleusner
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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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Ezra Engel, Lishan Li, Chris Hudy, Robert Schleusner. 2025-04-30. Multi-modal Transfer Learning for Dynamic Facial Emotion Recognition in the Wild. https://arxiv.org/abs/2504.21248

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