Access Control
Identify registered people and connect the result to doors, gates, attendance and visitor systems.
People intelligence for a safer tomorrow
EnterpriseFace RecognitionKnow who is there.
Secure access. Faster passenger processing. Real-time awareness—built around your cameras, workflows and environment.
Solutions
One engine. Three critical workflows.
The same core face recognition engine.Built for the real world.
How it works
From camera to action.
Turn everyday video into trusted identity intelligence.Designed for your environment, your workflows, your goals.
Capture
Cameras capture faces in real time.
Analyze
Faces are detected and analyzed.
Match
Compared against your enrolled identities and watchlists.
Respond
Send the right information to your systems and people in real time.
Turningcameras intogreater possibilities.
Capabilities
Capabilities built in layers.
A complete set of face recognition technologies,ready for your most demanding use cases.
- Face Detection
- 1:1 Verification
- 1:N Identification
- Watchlist Matching
- Face Tracking
- Liveness
- Presentation Attack Detection
- Age & Gender Estimation
- Accessories Detection
Deployment
Deploy where your operation demands.
Flexible deployment and easy integrationwith your existing infrastructure.
Edge
Run at the edge for low latency.
On-Premise
Keep data in your environment.
Cloud
Scale with your operation.
ATLAS AIBOX
AI at the edge. Real-world ready.
- Easy integration
- SDK & API
- IP Cameras & VMS
- Access Control
- ATLAS AIBOX
Trust
Proven through independent evaluation.
Measured on data we did not choose,then proven on your own cameras before you commit.
NISTNational Institute of Standards and TechnologyFRTE Participant
Ayonix has participated in NIST Face Recognition Vendor Test evaluations since 2017, in both the 1:1 verification and 1:N identification tracks. NIST publishes comparative evaluation information and does not endorse individual vendors.
Review the evidence- 2007Founded in Tokyo
- 2017First NIST FRTE 1:1 submission
- 20181:N identification submissions begin
- TodayPublic report cards, still online
Continuing to supporta more open and trusted future.
Questions
Before you decide.
What is the difference between 1:1 verification and 1:N identification?
1:1 verification compares a live face with one claimed identity — a passport photo, an ID document or an enrolled record — and answers whether they match. 1:N identification compares a face with an enrolled gallery and returns the possible identities. Access control and watchlists are 1:N workflows; e-gates and document checks are 1:1.
Can Ayonix work with existing IP cameras?
Many projects can use existing camera infrastructure, but usable results depend on resolution, angle, lighting, compression, motion and face size. A representative video assessment should happen before commitment.
Can the system run on edge, on-premise, or cloud infrastructure?
Yes. The same engine runs at the edge on ATLAS AIBOX hardware, on servers in your own controlled environment, or in a cloud environment you have approved. The choice is driven by your latency, security and data-governance requirements, and a solution design maps the workflow to the model that fits.
How should a face recognition system be evaluated before deployment?
Evaluate the intended camera, population, threshold and operating conditions together. A controlled pilot should measure false matches, missed matches, throughput and operator handling — not rely on one headline percentage.
How are privacy, human review, and data retention handled?
Define a lawful purpose, limit data collection and retention, control access, log operator actions and keep human review for consequential decisions. Exact controls must be aligned with applicable laws and organizational policy.
Let’s build together
Start with your real environment.
Tell us about your cameras, gallery size, latency and integration requirements.
