DCAlexNet: Deep coupled AlexNet for micro facial expression recognition based on double face images

Facial Micro-Expression Recognition (FER) presents challenges due to individual variations in emotional intensity and the complexity of feature extraction. While apex frames offer valuable emotional information, their precise role in FER remains unclear. Low-resolution facial images further degrade performance compared to high-resolution (HR) images. Existing methods, including super-resolution and convolutional neural networks, yield only moderate results. This work proposes a deep coupled AlexNet (DCAlexNet) model with a trunk network trained on multi-resolution images to extract discriminative features and a branch network for resolution-specific mapping between HR and low-resolution (LR) images. By integrating global and local facial information, DCAlexNet enhances micro-expression recognition while filtering irrelevant facial regions. The evaluations on FER2013, BU-3DFE, and Oulu-CASIA datasets demonstrate superior performance, achieving 98.3 % accuracy on FER2013, 97.2 % on BU-3DFE, and 96 % on Oulu-CASIA, with improved RMSE, RAE, and processing times.