The application of improved AFCNN model for children's psychological emotion recognition

Children's mental health has become an increasingly prominent concern in modern education. However, insufficient attention from schools and families to children's psychological and emotional issues has exacerbated the problem. This study proposes a psychological emotion recognition model for children based on an Attention-Enhanced Convolutional Neural Network (AFCNN). The model initially employs a traditional Convolutional Neural Network (CNN) to extract image features and integrates an attention mechanism to enhance focus on key features, thereby improving recognition accuracy and precision. The proposed AFCNN model achieves an accuracy of 86.5%, representing a 14.4% improvement over traditional CNN models. By incorporating a channel attention mechanism, AFCNN significantly enhances the recognition of micro-expressions, particularly excelling in cross-age generalization and real-time performance. Experimental results demonstrate that AFCNN has broad application prospects in monitoring children's mental health and offers a precise tool for emotion recognition in educational and psychological counseling contexts.