These annotations are part of the 68 point iBUG 300-W dataset which the dlib facial landmark predictor was trained on. The indexes of the 68 coordinates can be visualized on the image below:įigure 2: Visualizing the 68 facial landmark coordinates from the iBUG 300-W dataset ( higher resolution). The pre-trained facial landmark detector inside the dlib library is used to estimate the location of 68 (x, y)-coordinates that map to facial structures on the face. Understanding dlib’s facial landmark detector The end result is a facial landmark detector that can be used to detect facial landmarks in real-time with high quality predictions.įor more information and details on this specific technique, be sure to read the paper by Kazemi and Sullivan linked to above, along with the official dlib announcement. Given this training data, an ensemble of regression trees are trained to estimate the facial landmark positions directly from the pixel intensities themselves (i.e., no “feature extraction” is taking place). Priors, of more specifically, the probability on distance between pairs of input pixels.These images are manually labeled, specifying specific (x, y)-coordinates of regions surrounding each facial structure. A training set of labeled facial landmarks on an image.The facial landmark detector included in the dlib library is an implementation of the One Millisecond Face Alignment with an Ensemble of Regression Trees paper by Kazemi and Sullivan (2014). There are a variety of facial landmark detectors, but all methods essentially try to localize and label the following facial regions: Given the face region we can then apply Step #2: detecting key facial structures in the face region. Instead, what’s important is that through some method we obtain the face bounding box (i.e., the (x, y)-coordinates of the face in the image). In either case, the actual algorithm used to detect the face in the image doesn’t matter. Or we might even use deep learning-based algorithms for face localization. We might apply a pre-trained HOG + Linear SVM object detector specifically for the task of face detection. We could use OpenCV’s built-in Haar cascades. Step #2: Detect the key facial structures on the face ROI.įace detection (Step #1) can be achieved in a number of ways.Step #1: Localize the face in the image.In the context of facial landmarks, our goal is detect important facial structures on the face using shape prediction methods.ĭetecting facial landmarks is therefore a two step process: 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. Update July 2021: Added section on alternative facial landmark detectors, including dlib’s 5-point facial landmark detector, OpenCV’s built-in facial landmark detector, and MediaPipe’s face mesh detector.įigure 1: Facial landmarks are used to label and identify key facial attributes in an image ( source).ĭetecting facial landmarks is a subset of the shape prediction problem.To learn more about facial landmarks, just keep reading. In the next blog post in this series we’ll take a deeper dive into facial landmarks and learn how to extract specific facial regions based on these facial landmarks. How to detect and extract facial landmarks from an image using dlib, OpenCV, and Python.Exactly what facial landmarks are and how they work.In today’s blog post we’ll be focusing on the basics of facial landmarks, including: Today we are going to use dlib and OpenCV to detect facial landmarks in an image.įacial landmarks are used to localize and represent salient regions of the face, such as:įacial landmarks have been successfully applied to face alignment, head pose estimation, face swapping, blink detection and much more.
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