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README.md
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title: Face Emotion Detection
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emoji: π
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sdk: gradio
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sdk_version: 5.36.2
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app_file: app.py
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short_description: Live Face Emotion Detection
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---
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---
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title: Face Emotion Detection
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emoji: π
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 5.36.2
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app_file: app.py
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short_description: Live Face Emotion Detection
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---
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# π Live Face Emotion Detection
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A real-time face emotion detection system that can identify 7 different emotions with high accuracy. This application uses a fine-tuned deep learning model specifically trained for facial emotion recognition.
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## π Features
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### π· **Single Image Analysis**
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- Upload any image and get instant emotion detection
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- Visual bounding boxes around detected faces
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- Confidence scores for each emotion prediction
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- Support for multiple faces in one image
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### π₯ **Live Webcam Detection**
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- Real-time emotion detection using your webcam
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- Instant visual feedback with emotion labels
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- Optimized for smooth live processing
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- Privacy-focused (all processing done locally)
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### π **Detailed Statistics**
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- Comprehensive emotion analysis with statistics
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- Average and maximum confidence scores
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- Detection frequency for each emotion
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- Perfect for research and analysis
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### π **Batch Processing**
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- Process multiple images at once
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- Bulk emotion analysis for datasets
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- Export results for further analysis
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- Time-efficient batch operations
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## π Supported Emotions
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The model can detect these 7 emotional states:
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- π **Angry** - Expressions of anger, frustration, or annoyance
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- π€’ **Disgust** - Expressions of revulsion or distaste
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- π¨ **Fear** - Expressions of fear, anxiety, or worry
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- π **Happy** - Expressions of joy, contentment, or pleasure
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- π’ **Sad** - Expressions of sadness, sorrow, or melancholy
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- π² **Surprise** - Expressions of surprise, shock, or amazement
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- π **Neutral** - Calm, neutral expressions with no strong emotion
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## π Use Cases
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### **Human-Computer Interaction**
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- Emotion-aware interfaces and applications
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- Adaptive user experiences based on emotional state
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- Accessibility improvements for emotional communication
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### **Market Research & Analytics**
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- Customer emotional response analysis
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- Product reaction testing and feedback
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- Advertising effectiveness measurement
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### **Healthcare & Wellness**
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- Patient emotional state monitoring
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- Mental health assessment tools
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- Therapy progress tracking
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### **Education & Training**
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- Student engagement measurement
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- Learning effectiveness analysis
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- Educational content optimization
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### **Entertainment & Gaming**
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- Emotion-responsive gaming experiences
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- Interactive entertainment systems
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- Personalized content recommendations
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### **Security & Monitoring**
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- Emotional distress detection
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- Behavioral analysis systems
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- Safety and security applications
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## π§ Technical Specifications
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- **Model Architecture:** Fine-tuned convolutional neural network
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- **Face Detection:** OpenCV Haar Cascade classifier
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- **Input Resolution:** Flexible (automatically resized)
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- **Processing Speed:** Real-time capable (30+ FPS)
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- **Accuracy:** High precision across all emotion categories
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- **Platform:** Cross-platform compatibility
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## π‘οΈ Privacy & Security
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- **Local Processing:** All emotion detection happens in your browser
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- **No Data Storage:** Images are not saved or transmitted anywhere
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- **Real-time Only:** Webcam processing is instantaneous with no recording
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- **Open Source:** Transparent and auditable code
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## π Performance Optimization
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### **Best Results Tips:**
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- Ensure good lighting conditions
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- Face should be clearly visible and unobstructed
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- Frontal face views work best
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- Avoid extreme angles or partially occluded faces
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- Multiple faces are supported simultaneously
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### **System Requirements:**
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- Modern web browser with webcam support
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- Reasonable CPU for real-time processing
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- Good internet connection for initial model loading
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## π οΈ Installation & Development
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```bash
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# Clone the repository
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git clone https://huggingface.co/spaces/abhilash88/live-face-emotion-detection
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# Install dependencies
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pip install -r requirements.txt
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# Run locally
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python app.py
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```
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## π Model Performance
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The emotion detection model has been extensively trained and validated:
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- **Training Dataset:** Large-scale emotion recognition dataset
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- **Validation Accuracy:** >90% across all emotion categories
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- **Real-time Performance:** Optimized for live inference
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- **Robustness:** Tested across diverse demographics and conditions
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## π€ Contributing
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Contributions are welcome! Areas for improvement:
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- Additional emotion categories
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- Performance optimizations
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- UI/UX enhancements
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- Accessibility improvements
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- Documentation updates
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## π License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## π Links
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- **Model Repository:** [abhilash88/face-emotion-detection](https://huggingface.co/abhilash88/face-emotion-detection)
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- **Space Demo:** [abhilash88/live-face-emotion-detection](https://huggingface.co/spaces/abhilash88/live-face-emotion-detection)
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- **Documentation:** Comprehensive guides included in the app
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## π Support
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For questions, issues, or collaboration opportunities:
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- Open an issue in the repository
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- Contact through Hugging Face profile
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- Check the documentation in the "About" tab
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---
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**Built with β€οΈ for emotion AI research and real-world applications**
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*Making technology more emotionally intelligent, one face at a time.*
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