- Developed face recognition in E9V3 board.
- E9V3 board
- Used OpenCV to compress the image and send it to the server for recognition via socket
- The result is received and displayed on the screen.
- Server
- The facial features are extracted and labeled from the image, and fed into the pretrained model to identify people.
- Finally, the results are sent back to the board
- We can correctly detect faces and facial features
- the performance around 2-3 frames per second.
- The main time-consuming place is to receive pictures on the embedded device.
- If we just show the predicted image on the server, the can around 6-7 frames per second.
