# LLM-Based Pose Normalization and Multimodal Fusion for Facial Expression Recognition in Extreme Poses

## TL;DR

This paper introduces a new method for facial expression recognition that improves accuracy for extreme facial poses using pose normalization and multimodal learning.

## Contribution

A novel FER method combining pose normalization with vision–language learning to handle extreme poses.

## Key findings

- The method achieves 89.39% accuracy on the RAF dataset.
- It outperforms existing approaches on EXPW and AffectNet-7 datasets.
- CLIP enhances expression feature representation through joint vision–language learning.

## Abstract

Facial expression recognition (FER) technology has progressively matured over time. However, existing FER methods are primarily optimized for frontal face images, and their recognition accuracy significantly degrades when processing profile or large-angle rotated facial images. Consequently, this limitation hinders the practical deployment of FER systems. To mitigate the interference caused by large pose variations and improve recognition accuracy, we propose a FER method based on profile-to-frontal transformation and multimodal learning. Specifically, we first leverage the visual understanding and generation capabilities of Qwen-Image-Edit that transform profile images to frontal viewpoints, preserving key expression features while standardizing facial poses. Second, we introduce the CLIP model to enhance the semantic representation capability of expression features through vision–language joint learning. The qualitative and quantitative experiments on the RAF (89.39%), EXPW (67.17%), and AffectNet-7 (62.66%) datasets demonstrate that our method outperforms the existing approaches.