Real-Time Emotion Recognition in Psychological Intervention Methods

Abstract

In the context of mental health, this study aims to develop a real-time emotion-focused facial recognition system based on psychological intervention methods. It uses a convolutional neural network (CNN) base and is trained with the FER2013 dataset, which consists of 35,887 facial images classified into seven basic emotions. Through normalisation, data augmentation, and training in TensorFlow and Keras, the model achieved 92.3% accuracy in a pilot test with 1,000 images, achieving an F1 score of 0.92, precision of 0.93, and recall of 0.91. Subsequently, when scaled to 71,774 images, it maintained robust performance with an overall accuracy of 77.5%. Emotions such as happiness (0.83), surprise (0.80), and neutrality (0.85) were recognised with greater accuracy, while K-means analysis was applied to cluster emotional patterns in a visually interpretable way. Complementing the technical architecture, a user-friendly graphical interface was designed for psychology professionals, allowing clear visualisation of the detected emotions with a latency of just 150 milliseconds per image. Overall, this proposal represents a significant advance toward more interactive, personalised, and efficient therapies, without requiring a complex technological infrastructure. Future studies recommend exploring different multimodal signals and increasing the use of convolutional layers to improve the quality of results and data efficiency.