ERBMA-Net: Enhanced Random Binary Multilevel Attention Network for Facial Depression Recognition

Depression is a major contributor to the global disease burden and is closely associated with variations in facial expressions, making them a critical biomarker for its recognition. Traditional local binary convolutional neural networks (LBCNN) rely on fixed binary weights, which fail to capture complex and nuanced features, limiting their adaptability and generalization. Existing methods often lack extracting spatial hierarchies and model long-range dependencies within facial expressions. To address these gaps, an enhanced random binary multilevel attention network (ERBMA-Net) is proposed for facial depression recognition, which employs a multibranch architecture to extract global (face) and local (eyes and mouth) features. Subsequently, the enhanced random binary convolutional neural network (ERBCNN) introduces random binary filters and a refinement layer to improve adaptability over LBCNN. Furthermore, multilevel attention mechanisms, including spatial attention are applied independently to global and local features and self-attention on concatenated features to capture critical spatial and relational patterns. Comprehensive evaluations on the Audio-Visual Emotion Challenge 2014 (AVEC2014) and the Changzhou No. 2 People’s Hospital (CZ2024) datasets demonstrate the superior performance of ERBMA-Net, achieving mean absolute error and root mean square error: 6.80/8.18 and 6.37/8.08, respectively. These results establish ERBMA-Net as a robust framework for artificial intelligence-assisted mental health diagnostics. The code is available at: https://github.com/DrTuryalai/ERBMA-Net.