What Is a Facial Recognition Device?
Traditional facial recognition systems rely on powerful servers to process video streams, using intensive algorithms to detect and match faces in real time. While effective, this architecture is expensive and complex, requiring significant infrastructure and ongoing maintenance.
A smarter approach is to shift the heavy analytics to the edge—embedding facial recognition directly into the camera. In this model, the server acts primarily as a lightweight proxy to manage devices and aggregate events for centralized notifications. This requires highly efficient algorithms that can run on low-cost chips without sacrificing accuracy.
How Dedicated Facial Recognition Devices Reduce Total Cost of Ownership (TCO)
Edge-based facial recognition devices (EFRDs) may cost more than high-quality general-purpose surveillance cameras, but they offer substantial TCO savings when used for facial recognition. Here’s how:
- Reduced Bandwidth Requirements
By processing video locally, edge computing cameras drastically reduce the amount of data sent over the network. Beyond the benefit of not depending on network latency, this lowers bandwidth usage by over 90%, enabling the use of less expensive network links, avoiding costly upgrades to switches, and reducing monthly internet service costs. - No Need for Expensive GPU Servers
Because EFRDs handle processing on the device, there’s no need for costly centralized servers equipped with high-end GPUs. In traditional systems, server costs can exceed $400 per camera—costs that are entirely avoided with edge-based solutions.
Cut System Costs in Half
When you factor in licensing and infrastructure, deploying general-purpose cameras with server-based analytics can cost up to twice as much as using edge-based facial recognition devices. By consolidating processing and reducing dependencies, EFRDs offer a more scalable, efficient, and cost-effective solution.
Author:
Steve McMillen
Director of Solutions Engineering, SAFR
