I am trying to develop a facial recognition system on a raspberry pi 4 for a university project. I have to use Google Auto ML, Facenet, and Tensorflow. I have some understanding of what they are (I think), just want some guidance on what each really does and how they affect each other's operation when it comes to facial recognition. Any guidance would really appreciate it, just need to be shown the right path that is all!
Facial Recognition on Raspberry Pi
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You can find a lot of articles in medium/Github/Youtube/instructables/Tensorflow Examples on deploying Face recognition in Raspberry pi as a blueprint to get a head start . But You have to play with your Raspberry pi a bit to gain some Ground skills if you are unaware of Hardware details and other skills like Capturing Frames from Video ,Training and evaluating data etc.
I see a stable wheel of Tensorflow wheel by PINTOO for installing Tensorflow in Raspberry pi. A USB accelerator is recommended to smoothen the computation process.You can also use our TFlite for Edge devices like Raspberry pi.
Once model is Trained , you can convert into smaller size Tensorflow lite models or use Rest-API to a server to get results.Post Queries here on SO When you find an obstacle.
Attaching below links for reference.
https://www.tensorflow.org/lite/examples
https://github.com/PINTO0309/Tensorflow-bin#usage
https://bhashkarkunal.medium.com/face-recognition-real-time-webcam-face- recognition-system-using-deep-learning-algorithm-and-98cf8254def7
https://www.pyimagesearch.com/2018/06/25/raspberry-pi-face-recognition/