Mood-Enhancing Video Recommendation System Using Emotion Recognition
Computer Science
Project Results
Developed emotion recognition recommendation system, achieving 90% accuracy in suggesting mood-enhancing videos based on real-time facial expressions.
Project Description
In today's fast-paced technological landscape, there arises a crucial need for expert systems capable of understanding and responding to human emotions, thereby delivering personalised experiences.Â
My project addresses this need by developing a recommendation system designed to recognise human emotions and suggest mood-enhancing videos accordingly. By leveraging machine learning and deep learning techniques, including Haar cascade and deep face technology for human emotion recognition, my system analyzes facial expressions in real-time to identify users' emotional states. Once determined, the system recommends tailored videos to uplift and inspire, positively influencing mood. Moreover, after building the ML model, I've integrated a user-friendly frontend that seamlessly interacts with the emotion recognition and video recommendation components.Â
While my recommendations are currently limited to the dataset available, this project sets the stage for future advancements in emotion-aware recommendation systems, ultimately enhancing user experiences and emotional well-being in technology-driven environments.
Mentee
Ahaana Gupta
Remarks
Working on the emotion-driven video recommendation project with Headstart was an absolute blast! Instead of feeling overwhelmed, I enjoyed every step of the process. The platform was really easy to use, and everything was set up perfectly to help me stay focused and on top of things. Also huge thanks to my mentor for making this journey so awesome and for guiding me throughout the duration of the project. I felt comfortable enough to ask him all my doubts