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landmarks in an image.

Facial landmarks are used to localize and represent salient regions of the face, such as: Eyes, eyebrows, nose, mouth, jawline, facial landmarks have been successfully applied to face alignment, head pose estimation, face swapping, blink detection and much more.

In todays blog post well be focusing on the basics of facial landmarks, including: Exactly what facial landmarks are and how they work.

How to detect and extract facial landmarks from an image using dlib, OpenCV, and Python.

In the next blog post in this series well take a deeper dive into facial landmarks and learn how to extract specific facial regions based on these facial landmarks.

To learn more about facial landmarks, just keep reading.

Looking for the source code to this post?

Jump Right To The Downloads Section.

Facial landmarks with dlib, OpenCV, and Python.

The first part of this blog post will discuss facial landmarks and why they are used in computer vision applications.

From there, Ill demonstrate how to detect and extract facial landmarks using dlib, OpenCV, and Python.

Finally, well look at some results of applying facial landmark detection to images.

What are facial landmarks?

Figure 1: Facial landmarks are used to label and identify key facial attributes in an image ( source ).

Detecting facial landmarks is a subset of the shape prediction problem.

Given an input image (and normally an ROI that specifies the object of interest a shape predictor attempts to localize key points of interest along the shape.

In the context of facial landmarks, our goal is detect important facial structures on the face using shape prediction methods.
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