Face Recognition and Face Detection series in Python
- Detecting Faces with 20 lines in Python
- Face Recognition with 20 lines in Python
- Detecting Facial Features with 20 lines in Python
- Facial Features and Face Recognition with 20 lines in Python
- Performance improvements with code
- Resize the camera input with OpenCV
- Working with Haar Cascades and OpenCV
- Detect and blur faces 😁 using haar cascades
- Detect and blur faces 😁 using DNN
Hi!
I started to do some tests with the new Raspberry Pi 4 and the results are amazing. I’m not a performance expert, so I decided to pick up some of the demos / apps I’ve creating for the Raspberry Pi and run them in both models: Raspberry Pi 3 B+ and Raspberry Pi 4.
I started with an amazing set of tutorials on how to perform Face Recognition from Adrian Rosebrock (see references). I’ve been using his Face Recognition python package for this scenarios and it’s an amazing one.
I added some code to a custom version of Adrian’s Face Recognition sample, and it looks great. The main idea was to track in real-time the current FPS (similar to the work I did with the Image AI and Hololens sample a couple of days ago, see references).
This sample load a file with 15 trained faces and analyze frame by frame to
- Detect faces in the frame.
- If a face is detected, draw a frame around it.
- For each detected frame analyze if the face is a trained face.
- If the face is part of the trained dataset, the app will add the name of the person on top of the frame.
I display in real-time the FPS processed with a USB camera in a Raspberry Pi 3 B+. Doing a lot of tweaks and getting the best performance in the device I could never process 1FPS. The average processing data were between 0.6 and 0.9 FPS in a Raspberry Pi 3B+.
IMHO, these results are great for a small device like a Raspberry Pi 3B+. But now it was time to test it in the new Raspberry Pi 4. And an important note here is to remark that even if I did this tests in a Raspberry Pi 4 with 4GB of Rams, the performance results are similar to a RPI4 with just 1 GB of ram. We have more memory, however the processor improvements are quite significant in the new version.
I installed all the necessary software in the Raspberry Pi 4 and I got 3X better results. I’ve even tun this in a 1080p resolution to get a sense of the real processing time. The average processing data were between 2.3 and 2.4 FPS in a Raspberry Pi 4.
Amazing! In this scenario the Raspberry Pi 4 is almost 3 times faster than the Raspberry Pi 3. And again, these are amazing times for a 50USD device.
The sample source code is https://github.com/elbruno/Blog/tree/master/20190819%20Rpi%203%20vs%20Rpi%204%20Face%20Recognition
I even have time for some BBQ time with family and friends!
Happy coding!
Greetings @ Toronto
El Bruno
References
- Adrian Rosebrock, Raspberry Pi Face Recognition https://www.pyimagesearch.com/2018/06/25/raspberry-pi-face-recognition/
- El Bruno, Detecting Hololens in realtime in webcam feed using ImageAI and #OpenCV with performance improvements https://elbruno.com/2019/08/12/python-detecting-hololens-in-realtime-in-webcam-feed-using-imageai-and-opencv-with-performance-improvements/
El Bruno 
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