Enhanced Facial Emotion Detection Using Convolutional Neural Networks and Attention Mechanisms

Convolutional neural networks are now facing a number of technological obstacles in the field of facial expression identification, including the low automatic recognition rate, the difficulty of effectively recognizing minor changes in facial emotions, and the complexity of feature extraction. In order to enhance automatic facial expression identification, this research suggests a Height Performance Module Implement (HPMI) attention mechanism based on spatial attention. The model concentrates on important spatial characteristics by incorporating this attention mechanism, which improves recognition during training. The VGG-16 network incorporates a spatial attention-based HPMI module that efficiently reduces over fitting, amplifies key information, and suppresses irrelevant data. This facilitates effective information exchange between the network model and the image's key properties. It also deals with the problem of dimensional mismatch between output and input. On the CK+ expression datasets, the approach yields accuracy rates of 97.05%, respectively. According to experimental results, the incorporation of the HPMI module improved accuracy by an average of 3.94%.