This guide introduces some of the design considerations and best practices to consider when training and quantizing an MCU-friendly face recognition model.
In recent years, facial recognition technology has become ubiquitous in daily life. There are numerous ways that it has made our lives easier, including:
- Electronic passport control gates
- Securely unlocking your smartphone
- Automatically tagging your friends in photographs
Great advances have been made in the accuracy and reliability of these systems. These advances have mainly come about due to the adoption and use of neural network models.
However, these accuracy gains come at the cost of increasing compute and power requirements. This increased need for compute power means that performing inferences on these models is traditionally offloaded to servers in the cloud. Offloading the task to the cloud comes with its own problems though, mainly in latency and data security.
Therefore, there is now a significant push to develop hardware and software that enables execution of these networks quickly and securely on edge devices. Security and latency are improved because user data never has to leave the device. This is because there is no need to rely on patchy internet connections.
Before you begin
Before you read this guide, you should be familiar with MCU-friendly face recognition models and with the concept of quantization.