EmoDetect: ML Based Facial Assessment of Anxiety & Depression

EmoDetect is an innovative online application de- signed to provide real-time emotion analysis and mental health monitoring through facial expression recognition. In today’s digi- tal age, where mental health concerns are increasingly prevalent, EmoDetect offers a user-friendly platform for individuals to gain insights into their emotional well-being. Leveraging state- of-the-art deep learning techniques and the Deep Face library, EmoDetect accurately detects and analyzes facial expressions to determine users’ emotional states. The application allows users to upload video files or utilize their webcam for real- time emotion analysis. EmoDetect calculates depression and anxiety scores based on detected emotions, providing users with personalized insights into their mental health status. Through intuitive visualizations, users can explore the distribution of emotions over time and gain a deeper understanding of their emotional patterns. EmoDetect not only empowers users to track their emotional well-being but also provides actionable recom- mendations, such as personalized music playlists, to uplift their mood. Furthermore, the application facilitates data storage and report generation, enabling users to track their emotional journey over time. In this paper, we present the architecture, methodology, and key features of EmoDetect, along with experimental results demonstrating its effectiveness in emotion analysis and mental health monitoring. We discuss the implications of our findings for mental health care and highlight potential avenues for future research and application development in this domain.