Advanced Face Detection & Analysis
This comprehensive computer vision application provides powerful face detection and analysis capabilities using state-of-the-art machine learning models. Built with Python, OpenCV, and Streamlit, it offers an intuitive interface for analyzing faces and facial features in images and videos.
The application leverages OpenCV's Deep Neural Network (DNN) module with a pre-trained SSD MobileNet model for accurate face detection, along with Haar Cascade classifiers for detailed facial feature recognition.
Face Detection
Accurately detects faces in images and videos using OpenCV's DNN module with SSD MobileNet, with adjustable confidence thresholds for optimal performance.
Facial Feature Detection
Identifies key facial features like eyes and smiles using Haar Cascade classifiers with customizable sensitivity controls.
Age & Gender Analysis
Estimates age and gender using DeepFace integration, providing additional demographic insights from facial images.
Video Processing
Processes both uploaded videos and webcam streams (when run locally) with real-time facial analysis and feature detection.
Face Comparison
Compares faces across images using facial embeddings and similarity metrics to identify matching individuals.
Face Recognition
Recognizes known faces from a database with persistent storage capabilities for managing facial identities.
Technologies
- Python for backend processing
- OpenCV for computer vision operations
- Streamlit for web interface
- SSD MobileNet for face detection
- Haar Cascades for facial feature detection
- DeepFace for facial attribute analysis
- Scikit-learn for similarity metrics
- Matplotlib for visualization
- Hugging Face Spaces for deployment
Implementation
Key technical aspects of this project:
- Deep Neural Network approach for robust face detection
- Multi-model face embeddings for more accurate face comparison
- Optimized image processing pipeline for better feature detection
- Adaptive thresholding and confidence controls for all detection modules
- Performance optimization for real-time video processing
- Modular architecture for easy extension with new capabilities
- Comprehensive error handling for robust operation
Key Features
The application has multiple modes of operation:
- Face Detection Mode: Locates faces in images and videos with bounding boxes and confidence scores
- Feature Detection Mode: Identifies facial features (eyes, smiles) with adjustable sensitivity
- Comparison Mode: Compares faces between images to determine similarity
- Face Recognition Mode: Identifies known individuals from a database
- Real-time Processing: Works with webcam input when run locally
Note: Some features like webcam access are only available when running the application locally due to Hugging Face Spaces limitations.
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