Facial Expression Recognition with Age-Group Expression Feature Learning

Facial Expression Recognition (FER) is crucial for accurately understanding human emotions and intentions, making it a fundamental task in computer vision. Some studies have shown differences in facial expression patterns across different age groups. However, existing research on FER often overlooks the impact of age differences on model performance, leading to significant age bias issues and negatively affecting the overall performance of the models. To address this problem, we propose a FER framework based on Age-Group Expression Feature Learning (AEFL). Firstly, we adopt an age-group distribution guidance multi-branch to extract age-group expression features, thereby preserving the unique expression information within each age group. Next, we capture shared expression information among different age groups by learning global features from samples across all age groups. Lastly, we introduce an age-guided attention mechanism to enhance the representation of age-group information in the global features, mitigating the suppression of disadvantaged age-group information by advantaged age-group information in the global features. Experimental results demonstrate that our method significantly improves the recognition performance of disadvantaged age groups without compromising the performance of advantaged age groups, thus enhancing the overall model performance. Moreover, our method achieves state-of-the-art results on multiple large-scale facial expression benchmarks (RAF-DB, AffectNet-7, AffectNet-8).