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Compared with the traditional statistical models, such as the active shape model and the active appearance model, the facial feature point localization method based on deep learning has improved in accuracy and speed, but there still exist some problems. First, when the traditional deep neural network model targets a data set containing different face poses, it only performs the preprocessing through the initialized face alignment, and does not consider the regularity of the distribution of the feature points corresponding to the face pose during feature extraction. Secondly, the traditional deep neural network model does not take into account the feature space differences caused by the different position distribution of the external contour points and internal organ points (such as eyes, nose and mouth), resulting in inconsistent detection accuracy and difficulty of different feature points. In order to solve the above problems, this paper proposes a convolutional neural network (CNN) based on grayedge-HOG (GEH) fusion feature.

Gray-Edge-HOG feature based cascaded learning for facial landmark detection