Face recognition for large number of people
Increasing the dataset does not cause the curse of dimensionality. The curse of dimensionality occurs in high dimensional spaces for example when using a large number of features. Increasing the dataset has instead a positive effect.
I do see a problem in only using 5 pictures per class.
Also if you are interested in real-time performance (usually people mean 30fps+ when talking about real-time), I would look into running yolov4-tiny on a GPU instead of a CPU if that is possible.
I am building a face-recognition for a large number of people and I when to detect more and more when I add more data to train the model.
My current pipeline is:

Detecting faces with Yolov4-tiny
Recognize faces with KNN classifier (I train it with around 80 classes with each class contains around 5 pictures)

Currently, it can run in real-time with around 10fps on CPU. My concern is that through some research, I found that KNN will have problems if I increase the dataset (the curse of dimensionality). So i would like to know if it is ok for using KNN for this problem ? If not is there a way around this or another way to sort this problem ?