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Method of Face Alignment Using Cascaded Position Regression Based on Random Forest

A random forest and face alignment technology, which is applied to computer components, instruments, computing, etc., can solve the problems of inability to cope with face pose changes, partial occlusion of the face, poor robustness of the regressor, etc.

Active Publication Date: 2018-12-04
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

Therefore, the robustness of the regressor obtained by this method is poor, and it cannot cope with the situation where there are large changes in face pose and partial occlusion of the face.

Method used

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  • Method of Face Alignment Using Cascaded Position Regression Based on Random Forest
  • Method of Face Alignment Using Cascaded Position Regression Based on Random Forest
  • Method of Face Alignment Using Cascaded Position Regression Based on Random Forest

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Embodiment

[0050] see figure 1 , a method for face alignment based on random forest cascade position regression, including the following steps:

[0051] 1) Get the normalized face picture: read the pictures in the training set image library and the corresponding face attributes, and normalize the pictures. The face attributes include the rectangular area information of the face position, that is, x 1 axis, y 1 Axis, w width, h height information and known key point coordinate information of the calibration is x 2 axis, y 2 Axis information;

[0052] 2) Calculate the average shape of the face: determine 20 initial shapes for each face training sample, except for its own shape, that is, form 810×20 training samples, and rotate and scale the key point coordinate information of the training samples Similarity transformation, calculate the average shape of the face:

[0053]

[0054] N represents the number of training samples, here take 810, Indicates the key point face shape infor...

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Abstract

The invention discloses a method for face alignment based on random forest cascade position regression, which is characterized in that it comprises the following steps: 1) Obtaining a normalized face picture; 2) Calculating the average shape of the face; 3 ) Generate the candidate feature points of the face alignment frame; 4) Generate the face shape index gray value; 5) Generate the face shape index feature X; 6) Build the face alignment frame; 7) Initialize the face shape, after continuous iteration , outputting the final estimated face shape. This method can maintain good robustness under lighting, expression changes, occlusion, etc., and can improve accuracy and reduce failure rate.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to a random forest-based cascade position regression method for face alignment. Background technique [0002] Face alignment plays a very important role in face recognition, face tracking and 3D face reconstruction, attracting more and more researchers. However, due to the diversity of facial expressions, different lighting conditions and occlusions, there are also It brings difficulties and challenges to the research. [0003] Face alignment algorithms are roughly divided into two categories, one is the optimal face alignment algorithm, and the other is the regression-based face alignment algorithm. [0004] The optimal face alignment algorithm achieves the goal of face alignment by optimizing the error. Its performance depends on the design of the error equation itself and its optimization effect, which is difficult to guarantee. For example, the face alignment ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/16G06V40/168G06V40/172
Inventor 莫建文彭倜张彤袁华陈利霞首照宇欧阳宁高宇匡勇建
Owner GUILIN UNIV OF ELECTRONIC TECH