Mitigating Aging Bias in Facial Expression Recognition through Subgroup-Aware Data Augmentation

With the accelerating pace of global population aging, emotion-aware technologies have become increasingly important for improving the quality of life and psychological well-being of older adults. However, most facial expression recognition (FER) systems exhibit substantial performance degradation among elderly users due to the lack of age-diverse data and inadequate model adaptation. This study investigates age-related bias in FER and proposes a subgroup-aware data augmentation framework to enhance recognition robustness for older populations. We first retrain a ResNet-50–based age estimation model using the UTK-Face dataset to provide reliable age annotations for three benchmark FER datasets: RAF-DB, AffectNet, and ExpW. Subsequently, we introduce an age-adaptive augmentation strategy that applies stronger transformations to elderly facial images while maintaining moderate augmentation for younger ones. Experimental results demonstrate that the proposed approach significantly improves recognition accuracy and generalization in elderly subgroups without sacrificing performance in younger populations. This work provides a practical and scalable pathway toward age-inclusive affective computing, highlighting the importance of integrating demo-graphic priors into data processing pipelines for fair and trustworthy emotion recognition systems.