Research on Expression Recognition Method Based on Feature Decoding.

To address the issue of low accuracy in facial expression recognition due to insufficient consideration of semantic features and individual facial characteristics in most current facial expression extraction methods, a highly efficient facial expression recognition method based on feature decoding (FER-FD) is proposed. This method consists of two modules, namely the feature decoupling module (FFD) and the semantic enhancement module (VTS). Firstly, the FFD module employs two deep 2 D convolutional neural networks to extract facial and expression features from input images, where the facial feature decoupler disentangles facial features from expression features to minimize the influence of individual facial characteristics. Secondly, the VTS module adopts two key ideas to automatically capture facial movements in an unsupervised manner, thereby acquiring deep semantic information of the global facial region. Finally, concatenating the features from both modules enables more accurate prediction of facial expressions of samples. Experimental results demonstrate that the proposed feature decoding method achieves 98.78% accuracy on the CK + dataset, exhibiting scalability, adaptability to different scenarios, and good generalization capability. [ABSTRACT FROM AUTHOR]