Familiar Face Identification on MCUs: A Privacy-Preserving Solution for Personalizing Your Devices
Tim de BRUIN, Deep Learning Researcher, Plumerai
Imagine a TV that shows tailored recommendations and adjusts the volume for each viewer, or a video doorbell that notifies you when a stranger is at the door. A coffee machine that knows exactly what you want so you only have to confirm. A car that adjusts the seat as soon as you get in, because it knows who you are. All of this and more is possible with Familiar Face Identification, a technology that enables devices to recognize their users and personalize their settings accordingly.
Unfortunately, common methods for Familiar Face Identification are either inaccurate or require running expensive models in the cloud, with all of the security and energy compromises that come with cloud computing.
At Plumerai, we are on a mission to make AI tiny. We have recently succeeded in bringing Familiar Face Identification to microcontrollers. This makes it possible to identify users entirely locally — and therefore securely, using very little energy and with very low-cost hardware.
Our solution uses an end-to-end deep learning approach that consists of three neural networks: one for object detection, one for face representation, and one for face matching. We have applied various advanced model compression and training techniques to make these networks fit within the hardware constraints of microcontrollers, while retaining excellent accuracy.
In this talk, we will present the techniques we used to achieve Familiar Face
Identification on microcontrollers and demonstrate our product in action by giving a live demo. We will also discuss some of the practical challenges and lessons learned from building this product and how they differ from the academic literature on Familiar Face Identification. We believe our solution opens up new possibilities for user-friendly and privacy-preserving applications on tiny devices.