Zeyu Zhang¹ and Yu Sun², ¹USA, ²California State Polytechnic University, USA

This research paper presents the development and evaluation of a personalized mental health support application that leverages AI-driven features for real-time user interaction [1]. The application includes components for facial expression classification and real-time image generation, both of which were subjected to rigorous testing through targeted experiments [2]. The first experiment evaluated the accuracy of the emotion recognition system, revealing strong performance with distinct emotions but highlighting challenges with subtle expressions. The second experiment tested the responsiveness of the image generation component, showing effective performance with simple inputs but identifying delays with more complex tasks. While the application demonstrates significant potential, especially in its ability to provide tailored emotional feedback and support, further refinement is needed to enhance accuracy, performance, and data security. The findings suggest that with continued development, this application could become a valuable tool in the field of mental health and emotional well-being.

Facial Expression Classification, AI-Driven Mental Health, Real-Time Image Generation, Emotion Recognition, Emotional Well-Being