Improved Attention-Enhanced Efficient Face-Transformer Model for Multimodal Elderly Emotion Recognition in Smart Homes (Informatica, 2025)
Recognizing the emotions of the elderly is key to achieving personalized services insmart home environments. Traditional methods have difficulty capturi ng the correlation and temporal information of multimodal features.To this end, the study proposes a multimodal emotion recognition model that integrates the EfficientFace and Transforme r structures to construct an improved attention mechanism. A modal in teraction compensation termis introduced into the similarity calculation to improve the ability to model dynamic dependencies between modalities. Meanwhile, the dynamic importance factor is used to adaptively adjust feature weights. The model was tested o n IEMOCAP and self constructed EMED datasets. The emotion recognition precision reached up to 94.37%, the recall rate reached 93.64%, the F1 value was 94.21, and the specificity reached 9 4.85%. Additionally, the model achieved 96.24% classification accurac y and 94.13% emotional intensity on easily confused categories, such as "disgust" and "contempt," with a minimum detection latency of 0.55 seconds. Theresults show that the model exhibit s excellent performance in multimodal fusion and emotion recognition for the elderly, and is suitable for the task of smart home emotion monitoring.