The Facial Emotion Music Player is an innovative music recommendation system that takes into account the user's facial emotions, preferred language, and favorite singer to curate a personalized playlist on YouTube. By leveraging deep learning techniques, the model detects the user's facial emotions in real-time and suggests music that matches their mood.
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Emotion-Based Music Recommendation: Detects six different emotions—happy, sad, surprised, angry, neutral, and party—to suggest songs that resonate with your current mood.
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Language & Singer Preferences: Allows users to specify their preferred language and singer to further refine the music recommendations.
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Real-Time Emotion Detection: Utilizes Mediapipe's facial and hand landmarks to capture and analyze facial expressions in real-time.
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Streamlit Deployment: The model is deployed on Streamlit, offering an intuitive and interactive user interface.
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Seamless YouTube Integration: Once the emotion is detected and preferences are set, the player opens a corresponding playlist or song directly on YouTube.
- Input: The user provides their preferred language, favorite singer, and displays their current emotion through facial expressions.
- Emotion Detection: The model, trained on a dataset collected using Mediapipe's facial and hand landmarks, identifies the user's emotion.
- Music Recommendation: Based on the detected emotion, the model suggests songs that align with the user's mood, opening the selection on YouTube.