Here, I introduce another library named "Dlib", which is a computer vision library always cooped with opencv.
So to run the demo, we need to install Dlib on our system. 
I found tutorials to install dlib, and it worked for my device (Win10). 

https://www.learnopencv.com/install-opencv-3-and-dlib-on-windows-python-only/ (I have tried and it did work well)
https://www.pyimagesearch.com/2017/05/01/install-dlib-raspberry-pi/ (I haven't get a chance to test on my Pi)

Note that to install on windows, make sure you have CMAKE and Visual Studio 2017 installed.


2. How to use:
	a. Add custom face dataset
		1. Open "Facial_Recognition_Registration.py".
		2. Change string variable "label" (at line 7) to your name, such as: label = "feier_zhang"
		3. If using the laptop camera, make sure "cap = cv2.VideoCapture(0)" (at line 19);
		   If using the external WebCam, make sure "cap = cv2.VideoCapture(1)" (at line 19).
		4. Run "Facial_Recognition_Registration.py"
		
		Your face dataset:
		1. Folder "/Facial_images" -> "/face_rec" -> "/train", then you can see the folder of your name is in it.
	
	b. Train your face recognizer
		1. In "Facial_Recognition_Wrapper.py".
		2. Make sure line 230 (in the main function): mode = 'train'
		3. Run 
	
	c. Test on videostream
		1. In "Facial_Recognition_Wrapper.py".
		2. Make sure line 230 (in the main function): mode = 'test'
		3. Make sure line 182 to match your imaging device, same as above a.3
		4. Run

welcome any try-out and comments!