Compound Expression Recognition via Large Vision-Language Models

• URL: http://arxiv.org/abs/2503.11241v1
• Date: Fri, 14 Mar 2025 09:46:05 GMT
• Title: Compound Expression Recognition via Large Vision-Language Models
• Authors: Jun Yu, Xilong Lu,
• Abstract summary: Compound Expression Recognition (CER) is crucial for understanding human emotions and improving human-computer interaction.<n>To address these issues, we propose a novel approach leveraging Large Vision-Language Models (LVLMs)<n>Our method employs a two-stage fine-tuning process: first, pre-trained LVLMs are fine-tuned on basic facial expressions to establish foundational patterns; second, the model is further optimized on a compound-expression dataset to refine visual-language feature interactions.
• Score: 9.401699207785015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compound Expression Recognition (CER) is crucial for understanding human emotions and improving human-computer interaction. However, CER faces challenges due to the complexity of facial expressions and the difficulty of capturing subtle emotional cues. To address these issues, we propose a novel approach leveraging Large Vision-Language Models (LVLMs). Our method employs a two-stage fine-tuning process: first, pre-trained LVLMs are fine-tuned on basic facial expressions to establish foundational patterns; second, the model is further optimized on a compound-expression dataset to refine visual-language feature interactions. Our approach achieves advanced accuracy on the RAF-DB dataset and demonstrates strong zero-shot generalization on the C-EXPR-DB dataset, showcasing its potential for real-world applications in emotion analysis and human-computer interaction.

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