The advancement of edge AI hardware and software presents a significant opportunity to improve the accuracy and security of biometric identification technologies. How? The simplest answer is that edge computing can help by processing data closer to where it is created.
Edge computing can improve application performance of video analytics and other services by reducing latency. For biometrics, data management and privacy are equally important reasons to employ edge computing. The question then becomes where to deploy the application; while the answer is increasingly “on the device itself,” it is still useful to understand how cameras, voice and hand recognition systems will interact with nearby systems to provide advanced functionality and improve accuracy.
This white paper from Biometric Update, written by EdgeIR.com editor Jim Davis, is intended as a primer for understanding edge computing basics and where it will impact biometric identification systems.
The paper examines the architecture of edge computing (where edge computing is being used) and then applies this framework to contextualize use cases for biometrics and edge computing such as:
- Biometrics at the Smart Device and On-premise data center edge
- Biometrics at the Access and Regional edge
- Smart City
The paper also highlights some of the key trends in edge AI processors and algorithms that will power further advancements in edge-powered biometrics.
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