Face detection topic is really wide and the problem is quite complex, but in a few words I will try to describe it.
In digital image processing you have to treat image as an two-dimensional array of pixels. If you proceed RGB image each pixel contains 3 values (from r,g,b channels).
For the simple solution you can assume that the faces are always the same size (but consider it just as first step of your development or a prototype). After that step consider detection in multiple scales.
You have to choose frame size for face recognition and count some features inside each frame (if the shape is elipse in this case) and classify if it is face or not. Then you have to set your eyes, lips and nose regions and retrive it). You have to make several assumptions (as frame size, regions positions), which you have to think about (or do some research). There’s no just one way to do it.
I have done a paper on face detection (in fpga hardware, but there are some references to other papers and the algorithm is described quite nice). You can check it to get general idea of face detection and image processing.
There are also some already implemented methods (e.g. in openCV) if you don’t want to write it yourself.
Setting regions should base on your assumptions. Eg. you have the frame with face, so you know that the nose will be more or less in the center of the frame and will take eg. 10×20 pixels. Than the right (left) eye will be on the right (left) of the nose, a bit higher and will have 25×20 pixels. And the mouth will be 30×15 pixels 20 pixels beyond nose.
After setting your assumption you should test it, and may be change a bit or add some more parameters to find the regions like number of edges, corners. You can find some shapes eg. for eyes.
You know that the nose holes will be dark on the grayscale image. Use this information. You know that the lips are below nose and are wider than nose. Use that.
That’s what you have to do research about based on some images that would be your test images set. This set represent target images.
I think that noone would tell you exactly how to write it, because there are a lot of variables. You have to do it on your own, because you know where your app will be used. You know the conditions on witch the app will be working. Many things can affect the performance of your algorithm eg. lightning conditions, camera to face angle.
Do the research.
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solved Implement Facial Feature Detection [closed]