Here’s your README.md for the Face Detection & Recognition project in the same format:
This project is a computer vision-based application that detects and recognizes human faces from images or real-time video streams. It uses Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning techniques to automate identity verification with high accuracy. The system is widely applicable in security, attendance tracking, and surveillance systems.
The system operates in two main stages:
- Detects human faces in images or video frames
- Uses algorithms like Haar Cascade or CNN (Convolutional Neural Networks)
- Performs preprocessing such as resizing, normalization, and feature extraction
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Extracts unique facial features called embeddings
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Uses models like FaceNet or DeepFace
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Compares features with stored database using:
- Cosine Distance
- Euclidean Distance
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Identifies or verifies individuals based on similarity
- Real-time face detection using webcam
- Accurate face recognition system
- Automated identity verification
- Scalable and efficient solution
- Easy integration with security systems
- Python
- OpenCV (Face Detection)
- TensorFlow / PyTorch (Deep Learning)
- NumPy (Data Processing)
face-recognition/
│── app.py
│── dataset/
│── models/
│── utils/
│── templates/
│ └── index.html
│── static/
│── requirements.txt
│── README.md
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Install dependencies:
pip install -r requirements.txt
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Run the application:
python app.py
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Open browser:
http://127.0.0.1:5000/
- Security and surveillance systems
- Smart attendance systems
- Access control systems
- Personalized user experiences
- Sensitive to lighting conditions
- Performance affected by face angles and expressions
- Requires well-trained datasets for high accuracy
The AI-Powered Face Detection and Recognition System provides a fast, reliable, and automated solution for identity verification. With advancements in deep learning, it continues to improve in accuracy and real-world usability.
