Application of Deep Learning-Based Image Processing in Emotion Recognition and Psychological Therapy

With the rapid development of artificial intelligence technologies, particularly deep learning, the application of image processing in emotion recognition and psychological therapy has become a growing area of research. As a crucial indicator of an individual's psychological state, accurate emotion recognition plays a vital role in psychological treatment and mental health management. Traditional emotion recognition methods primarily rely on subjective judgment by human experts, which has certain limitations. In contrast, deep learning-based automated emotion recognition methods can capture emotional changes in real-time and with high accuracy through facial expressions, eye movement trajectories, and other image data, overcoming the shortcomings of traditional methods. Currently, emotion recognition technology is widely applied in fields such as psychological therapy, affective computing, and smart healthcare. However, existing research still faces challenges, including insufficient recognition accuracy, poor adaptability to individual differences, and weak integration with actual psychological therapy practices. In response to these issues, this paper proposes a deep learning-based image processing method that integrates multi-feature fusion techniques to improve the accuracy of emotion recognition. The method is applied to the detection of abnormal emotional states in psychological therapy and personalized emotion analysis. The results show that deep learning technology can effectively recognize complex emotional changes and provide more accurate emotional intervention strategies for psychological therapy, offering significant theoretical and practical value.