Comments (1)
Cool!
Don't worry, this does all processing on the device itself. It never connects to any other service nor transfers any data over the internet.
But of course since all the processing happens on the device and Raspberry Pis are pretty slow at running deep learning models, the performance might be worse than using a cloud-based solution. You can see in the RPi example code that I scale the camera images down to 320x240 pixels to make it run at a usable speed.
from face_recognition.
Related Issues (20)
- Import Error: libjpeg.so.62: version `LIBJPEGTURBO_6.2' not found HOT 1
- "Invalid image path: {}".format(X_img_path))
- [Question] - Using multiple angles of the face to generate a robust encoding to compare in videos
- It's not multi-thread due to Python GIL HOT 1
- foreign function interface (ffi)?
- Piping StdOut Sometimes Drops Output
- LFW Evaluation Example
- Design thinking
- 【BUG】Video example code is not working... HOT 2
- Jetson Nano 4GB: Segmentation fault (core dumped) - dlib and face_recognition HOT 1
- Doesn't works with ffmpeg 6.0_1
- Doesn't works
- Not working HOT 3
- Not accurate HOT 1
- Problem "ValueError: operands could not be broadcast together with shapes ( , ) (128, )" Solution
- fcaial recognition wrong person
- I want to detect whether the faces in the pictures are complete HOT 1
- Face similiarity for multiple images
- Fr
- test HOT 1
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from face_recognition.