Reliable facial recognition in any light, powered by AI
The HID U.ARE.U Face Module features advanced AI facial recognition technology for fast and accurate authentication and verification.
The HID U.ARE.U Face Module delivers advanced facial recognition identification and verification using AI-powered multispectral imaging (MSI) and machine learning.
With Paravision inside, our facial recognition system integrates their industry‑leading matching algorithm, trained on an extensive, ethically sourced dataset across diverse age and racial characteristics to help reduce matching bias. Paravision consistently ranks at the top of the National Institute of Standards and Technology (NIST) Facial Recognition Vendor Test (FRVT) for performance and accuracy.
Combining multiple cameras in a single device, U.ARE.U Face Module is easy to integrate and provides the highest level of matching performance and accuracy for the best user experience.
HID U.ARE.U Face Module Datasheet
Why Choose HID U.ARE.U Face Module?
- Top-Tier Recognition Performance: U.ARE.U Face Module is built with facial recognition algorithms ranked among the best in NIST FRTE benchmarks. It ensures consistent recognition in all lighting conditions ─ even total darkness. Ethically-trained AI reduces matching bias and supports inclusive identification.
- Wide Field of View: Users of different heights, from seated position to nearly seven feet tall, can be accommodated. No manual adjustments required.
- Matching at the Edge: Optional on-board matching enables identification and verification on device for an exceptional user experience
- AI Image Processing: Using edge processing drastically minimizes latency providing a matching-ready image
- Advanced Presentation Attack Detection (PAD): Industry-leading passive liveness detection to ensure authenticity and effective Presentation Attack Detection (PAD), conforming to ISO 30107-3 Level 2 standard
- Solution Enablement: Designed to fit your unique requirements with flexible interface options and developer-friendly U.ARE.U Software Development Kits to interface with your systems and applications
HID U.ARE.U Face Software Development Kits (SDKs)
HID U.ARE.U Face Software Development Kits (SDKs) deliver effective tools that empower your development team to quickly and easily integrate facial capture and recognition features to your existing applications.
The SDKs, available for iOS® and Android™, feature an intuitive interface that captures superior-quality facial
images for fast, accurate matching — delivering exceptional user experiences for mobile enrollment. The SDKs can also be used in conjunction with other HID facial recognition system components, such as the HID U.ARE.U Face Server for central biometric identity management.
- Automatic face detection with optional bounding box display
- Automatic face capture based on configurable quality thresholds
- Liveness detection/Presentation Attack Detection to prevent spoofing (add-on)
- Optional landmark detection
- Image quality analysis
- Biometric template extraction
- Biometric matching (1:1)
- Biometric verification (1:1) or identification (1:N) against a local template database of up to 1000 templates
- Biometric identification (1:N) against named subsets (galleries) in the local database
- Local database management (add user, remove user, create/delete/edit gallery)
- Debug tools reduce the learning curve and guide your team during development
TOP RANKING, AI-POWERED FACIAL RECOGNITION
Our facial recognition technology is built on the dependable, flexible and secure matching algorithm from our partner, Paravision®, a global leader of computer vision and machine learning.
Paravision’s top-performing face matching algorithms have consistently ranked high in the internationally recognized National Institute of Standards and Technology (NIST) Face Recognition Technology Evaluation (FRTE) reports, thanks to its advanced AI and machine learning technologies.
In addition, Paravision’s face matching algorithm has been trained using an extensive, ethically sourced dataset with millions of images, including a variety of gender, age and racial characteristics, to ensure sufficient diversity of data and defend against matching bias.