![digikam facial recognition digikam facial recognition](https://faceid-biometrics.com/wp-content/uploads/2019/03/Group-Shot-600x342.jpg)
We used a Logitech C920 HD Pro webcam for the setup but as we mentioned earlier, many webcams should work and should lead to similar results. Have a look at the framerate from our experiment we did two years ago with a Pi 3 and the Movidius stick, which was connected via USB 2.0. You can also use older Raspberry Pi versions but expect USB 2.0 to be a bottleneck which will substantially lower the achievable framerate. The Pi 4 is the first Pi which has USB 3.0 on board. While the accelerator also supports USB 2.0, it is recommended to use USB 3.0 to ensure sufficient data transfer rates. The USB accelerator is connected with the Raspberry Pi 4 via the USB 3.0 Type C interface. Using the accelerator, we achieved between 10 and 25 frames per second depending on how much image manipulation features we added and which image resolution we used. When we ran our experiments on the CPU of the Raspberry Pi 4 without the Coral USB accelerator, the application could process between 0.5 and 1.5 frames per second. Therefore, it allows high inference speed for image classification and object detection using neural networks. It can perform 4 trillion operations per second. It currently only supports pre-compiled TensorFlow Lite models. The accelerator contains an edge TPU (Tensor Processing Unit) coprocessor which is optimized to process matrix operations. The Coral USB accelerator connected to a Raspberry Pi 4 is the heart of the setup. A webcam with 60fps – have a look at webcams which are compatible with a Raspberry Pi.We used the following hardware components: In this blog post we explain how it works and how you can build your own face detection application with low cost consumer hardware and without much machine learning knowledge. In order to keep the face filters assigned to individual faces, even if multiple people appear in the video, it tracks the detected faces over time. The application detects faces based on a pre-trained neural network and overlays them with face filters.