Build a face detector that can extract up to 6 facial features using Python with OpenCV and DLib.

Today we are going to learn how to work with images to detect faces and to extract facial features such as the eyes, nose, mouth, etc. There’s a number of incredible things we can do this information as a pre-processing step like capture faces for tagging people in photos (manually or through machine learning), create effects to “enhance” our images (similar to those in apps like Snapchat), do sentiment analysis on faces and much more.

In the past, we have covered before how to work with OpenCV to detect shapes in images, but today we will take it to a new level by introducing DLib, and abstracting face features from an image.

Dlib is an advanced machine learning library that was created to solve complex real-world problems. This library has been created using the C++ programming language and it works with C/C++, Python, and Java.

It worth noting that this tutorial might require some previous understanding of the OpenCV library such as how to deal with images, open the camera, image processing, and some little techniques.

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