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. 
1. 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. If using the laptop camera, make sure "cap = cv2.VideoCapture(0)" (at line 17);
		   If using the external WebCam, make sure "cap = cv2.VideoCapture(1)" (at line 17).
		3. Run "Facial_Recognition_Registration.py"
		4. Enter the label as your name.
		
		Your face dataset:
		1. Folder "/Facial_images" -> "/face_rec" -> "/train", then you can see the folder of your name is in it.
	
	b. Run Facial_Recognition_Enrollment.py	 
	
	c. Test on videostream
		1. In "Facial_Recognition_Inference.py".
		2. Make sure line 27 to match your imaging device, same as above a.2
		3. Run


3. Requirements for face registration:
	a. User can sometimes slightly rotate their face, but must make sure their facical features (mouth, eyes, nose...) are within the camera view.
	b. Ambient light could affect the performance of the facial detection, such as overexposure, glare, reflection or so on;

welcome any try-out and comments!