Facial emotion recognition (FER) is an important component of human–computer interaction and healthcare-oriented affective computing. However, reliable deployment remains difficult in unconstrained settings due to appearance and geometric variability (e.g., pose, illumination, and occlusion), demographic imbalance, and dataset bias. In practice, two additional constraints frequently limit real-world FER systems: the computational overhead of heavy architectures and limited adaptability when data evolve over time, where sequential updates can cause catastrophic forgetting. To address these challenges, we propose the Incremental Attention-Enhanced Network (IAE-Net), a compact single-branch framework built on a DenseNet121 backbone and a cascaded refinement pipeline. The model incorporates Channel Attention (CA) to emphasize expression-relevant feature channels and suppress less informative responses, followed by a deformable attention module (DA) that reduces feature misalignment caused by non-rigid facial motion and pose shifts, thereby improving robustness under geometric variability. For continual deployment, IAE-Net supports class-incremental updates via weight transfer, exemplar replay, and knowledge distillation to improve retention during sequential learning. We evaluate IAE-Net on four widely used benchmarks, FER2013, FERPlus, KDEF, and AffectNet, covering both controlled and in-the-wild conditions under a unified training protocol. The proposed approach achieves accuracies of 79.15%, 92.03%, 99.48%, and 74.20% on FER2013, FERPlus, KDEF, and AffectNet, respectively, with balanced precision, recall, and F1-score trends. These results indicate that IAE-Net provides an efficient and extensible FER framework with potential utility in dynamic real-world and longitudinal healthcare-oriented applications.
Khan et al. (Wed,) studied this question.