EfficientHTNet: An EfficientNet–Hierarchical Transformer Cross-Attention Network for Facial Expression Recognition and Dementia-Label Classification

Facial expression recognition (FER) requires models that capture both fine local appearance and global facial structure. Convolutional neural networks provide strong local inductive bias, whereas vision transformers model long-range dependencies but can be data-intensive. This paper proposes EfficientHTNet, a compact dual-branch architecture that combines a partly fine-tuned EfficientNet-B0 local branch with a hierarchical Transformer global branch. A bidirectional cross-attention fusion module preserves and exchanges the 49 and 196 spatial tokens from the two branches, and an adaptive three-way gate combines the local, global, and interaction representations. The architecture is evaluated on FER2013 as the primary method-validation benchmark, on the synthetic SynPAIN corpus as a secondary breadth check, and on YT-DemTalk under subject-independent five-fold cross-validation as a proof-of-concept dementia-label classification task. The YT-DemTalk labels are derived from public videos and are not independently verified clinical diagnoses; therefore, those results must not be interpreted as Alzheimer’s disease diagnoses or clinical screenings. Across the evaluated tasks, the findings support a favorable accuracy, efficiency trade-off, while external validation, repeated runs, temporal modeling, and robustness analysis remain necessary.