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.
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