Face recognition models are designed to identify or verify individuals based on their facial features. They typically involve a pipeline of detecting faces, extracting features, and comparing those features to known faces.
Convert detected faces into numerical representations (embeddings) that capture the distinctive features of each face.
- Face Detection Libraries: OpenCV
- Deep Learning Frameworks: TensorFlow, PyTorch, Keras.
- Face Recognition Libraries: face_recognition (Python), OpenCV with deep learning models.
- Security: Access control, surveillance systems.
- Authentication: User login and verification.
- Social Media: Tagging and organizing photos.
- Customer Experience: Personalized services and interactions.
- Performance: Balance between speed (real-time processing) and accuracy.
- Privacy: Ensure compliance with data protection regulations like GDPR.
- Robustness: Handle variations in lighting, angles, and expressions.