Face Recognition — also known as face identification or face ID — engines in the Veritone cognitive engine ecosystem analyze human faces in images and video, and score them as to similarity with known faces.
They expand upon the capabilities of face detection engines by identifying the individual whose face was detected based on a library of known faces in addition to specifying where in the image the face is located.
Face Recognition Features
- Trainable with Custom Libraries
Create custom models unique to your use case with the Veritone Library application or your own library to identify a custom set of specific individuals. Learn more.
- Pre-Trained Celebrity Library
Identify noteworthy people from politics, sports, entertainment, business, media, and more in video and images with a pre-trained library of hundreds of thousands of famous individuals.
- Broad Data Source Support
Identify faces in videos and photos from sources such as body cameras, CCTV, booking photos, TV broadcasts, movies, mobile phone video, and more.
- Searchable Results
Identify where and when faces are recognized within files and data streams quickly with searchable face recognition engine output via API and Veritone applications.
- Near Real-Time Processing
Process audio and video files in near real-time for use cases requiring near-immediate face detection.
- Files or Stream Support
Identify faces in short-form or long-form videos in recordings, streamed recordings, or live data streams.
- Flexible Deployment
Deploy in a new or integrate into an existing application in the cloud via aiWARE GraphQL APIs, or with a subset that can be deployed on-premise via a Docker container. Learn more.
- Powered by an AI Ecosystem
Leverage advanced face recognition machine learning algorithms from the Veritone managed cognitive engine ecosystem — including algorithms from Veritone, niche providers, and industry giants.