I've implemented a multi-model face recognition system on the STM32N6 (originally based on the single model object detection demo) that efficiently runs detection and recognition models simultaneously on the NPU. Sharing the complete implementation since deploying and managing multiple AI models on the hardware has some non-obvious challenges.
- Detection Model: CenterFace for face detection (9ms inference)
- Recognition Model: MobileFaceNet (130ms per face)
- Pipeline Management: Efficient switching between models on the NPU
- Automated Deployment: Script that takes model paths, names, and addresses as JSON elements and builds + flashes them automatically
Repository: https://github.com/PeleAB/STM32N6-FaceRecognition
Next steps include creating an abstraction layer for JPEG and H264 compression to handle video streams more efficiently.
Happy to discuss multi-model deployment strategies or NPU optimization approaches. The automated build system alone might save others significant setup time.
