Multi-pose face feature point detection method based on cascade regression

A multi-pose face feature point detection method based on cascade regression, comprising the following steps of:

• (1) extracting pose index features and establishing corresponding optimal weak regressors:
• using a clustering algorithm to cluster face feature points to acquire feature point categories with adjacent positions;
• extracting pose index features under corresponding poses according to the feature point categories;
• and inputting the pose index features into a cascade regression algorithm, and training the pose index features to acquire the corresponding optimal weak regressors under different face poses;
• and
• (2) performing initialization and detection on face feature points under multi-pose changes:
• performing corresponding initialization according to different face pose orientations;
• using an SIFT feature of a face image as an input feature for face orientation estimation;
• acquiring an orientation of an input face image according to a random forest face orientation decision tree;
• using a feature point mean value of a face training sample under the orientation as an initial value of the input face image feature point;
• and extracting the pose index feature of the face image and inputting the pose index feature into the optimal weak regressor to acquire a distribution residual to update the current feature point distribution, and complete the face feature point detection.