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MULTI-POSE FACE FEATURE POINT DETECTION METHOD BASED ON CASCADE REGRESSION

  • US 20190318158A1
  • Filed: 11/30/2017
  • Published: 10/17/2019
  • Est. Priority Date: 12/14/2016
  • Status: Active Grant
First Claim
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1. 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.

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