Edge or Server? Choosing the Best Facial Recognition Architecture for Access Control

Implementing face biometric access management and attendance tracking software isn’t just about choosing the fastest or highest-scoring algorithms on NIST benchmarks.

Equally important — and often overlooked — is designing a system architecture that balances performance, security, scalability, and privacy.

A poorly designed setup can cause:

• Locked gates during network outages

• Delayed access revocations in secure zones

• Frustrated employees stuck at checkpoints

From offline remote locations to high-security facilities, different businesses face very different demands. The architecture you choose determines whether your system works smoothly — or fails at the worst possible time.

So how do you choose the right architecture? Let’s break it down.

When designing a face recognition access management or attendance tracking system, three business factors often shape the architecture:

🔹 Connectivity – Does your device need to function offline if the server is unreachable?

Example: One of our clients, a large macadamia plantation operator, needed access management and attendance tracking on remote sites with poor connectivity. Their devices handled face matching locally and synced with the server only once the connection was restored.

🔹 Data Synchronization – How fast do access rights need to update?

In low-risk environments, revoking access by the next visit is enough. But in high-security or hazardous sites, updates must reach the gate within seconds. And it’s not just about access removals — real-time visibility of who’s on-site can be critical for staff safety.

🔹 Error Tolerance (FAR/FRR) – What’s more costly:

• Letting an outsider in (False Acceptance)?

• Or frustrating your staff with rejections (False Rejection)?

Every business answers this differently — and that’s why the architecture matters.

Here, all facial recognition tasks—face detection, liveness detection, template extraction, and face matching—are performed directly on the device (camera, tablet, or biometric terminal). The system functions independently of a server or constant network connection, making it suitable for remote or offline sites.

Advantages:

• Works offline: No constant connectivity required, resilient to network outage.

• Ultra-low latency: Fast authentication at the door, even in high-traffic areas.

Challenges:

• Data synchronization risks: Revoking or updating access rights may take time.

• Limited scalability: Device compute power restricts accuracy and large-scale deployments.

• Distributed maintenance: Updates, patching, and model upgrades must be rolled out across many devices.

Best fit: Remote / offline sites where resilience and speed matter most.

Advantages:

• Maximum accuracy: Centralized use of advanced facial recognition models and ensembles.

• Always up-to-date access rights: Revocations and updates are applied instantly.

• High scalability: Handles large biometric databases across multiple sites.

• Centralized management: Simplifies updates, monitoring, and integration with HR, payroll, and security systems.

Challenges:

• Constant connectivity required: Network outages or latency can block or delay access.

• Data privacy concerns: Biometric transmission must comply with local regulations.

• Operational costs: Ongoing bandwidth and infrastructure expenses.

Best fit: Secure sites with strong connectivity, where accuracy and centralized control are critical.

There’s no one-size-fits-all architecture.

• Edge-centric shines in remote, offline, or high-traffic environments.

• Server-centric delivers maximum accuracy and centralized control in connected, security-critical environments.

The key is understanding your business risks, operational context, and regulatory environment — and then building the architecture that supports them.

At 3DiVi, we guide our clients through every step of their facial recognition journey. Ready to explore what this could look like for you? Let’s connect.