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--- |
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title: Face Emotion Detection |
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emoji: 🏆 |
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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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pinned: false |
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license: mit |
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short_description: Live Face Emotion Detection |
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--- |
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# 😊 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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## 🌟 Features |
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### 📷 **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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### 🎥 **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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### 📊 **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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### 🔄 **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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## 🎭 Supported Emotions |
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The model can detect these 7 emotional states: |
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- 😠 **Angry** - Expressions of anger, frustration, or annoyance |
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- 🤢 **Disgust** - Expressions of revulsion or distaste |
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- 😨 **Fear** - Expressions of fear, anxiety, or worry |
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- 😊 **Happy** - Expressions of joy, contentment, or pleasure |
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- 😢 **Sad** - Expressions of sadness, sorrow, or melancholy |
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- 😲 **Surprise** - Expressions of surprise, shock, or amazement |
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- 😐 **Neutral** - Calm, neutral expressions with no strong emotion |
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## 🚀 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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## 🔧 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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## 🛡️ 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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## 📈 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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## 🛠️ 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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## 📊 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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## 🤝 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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## 📄 License |
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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## 🔗 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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## 📞 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 ❤️ 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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