Abstract: A multi-pose face feature point detection method based on cascade regression comprises: extracting a pose index features and establishing corresponding optimal weak regressors; 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.

Abstract: A multi-pose face feature point detection method based on cascade regression comprises: extracting a pose index features and establishing corresponding optimal weak regressors; 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.