Face recognition is a biometric technology that matches a person’s unique facial features in images or videos against a face database to confirm their identity.
1.Face Detection
A face is captured from a live camera feed, uploaded photos, or video frames.
2.Face Alignment
The system positions up to 468 face landmarks and head rotation angles (roll, pitch, yaw) to create a normalized face representation.
3.Template Extraction
Unique face biometric features are extracted and encoded into a template for matching.
4.Face Matching
The biometric template is compared with another template (1:1) or multiple templates (1:N) to find a match.
1.Face Detection
A face is captured from a live camera feed, uploaded photos, or video frames.
2.Face Alignment
The system positions up to 468 face landmarks and head rotation angles (roll, pitch, yaw) to create a normalized face representation.
3.Template Extraction
Unique face biometric features are extracted and encoded into a template for matching.
4.Face Matching
The biometric template is compared with another template (1:1) or multiple templates (1:N) to find a match.
Key Features
Face Detection in Real-World Conditions
Detect faces with masks, hats, glasses and partial occlusions at different lighting, angles, and tilts.
High-Speed Face Matching
Search up to 800 million faces per second using full CPU resource.
Quick Biometric Template Generation
Build biometric templates for up to ~100 faces per second on a single CPU core and scale up to ~600 faces per second with GPU processing.
Flexible Deployment
Select on-premise, cloud, or edge deployments to meet your performance, privacy, and control requirements.
Multi-Language API Support
Compatible with Python, C++, C#, Kotlin, Flutter, Swift, Go, Java, and Node.js.
Cross-Platform SDKs & APIs
Available for Windows, Linux, Android, and iOS.
Benchmarks
3DiVi Face Recognition delivers top NIST-rated accuracy and speed with low false match rates and sub-second response times, even for databases with millions of faces. Performance is tested on diverse datasets to ensure robustness and eliminate demographic bias.