Three-dimensional face feature point positioning method based on noise reduction self-coding network

A feature point location, self-encoding network technology, applied in the field of computer vision

Active Publication Date: 2019-08-27
SOUTHEAST UNIV
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Problems solved by technology

Due to the influence of light, there are still large errors in the position

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  • Three-dimensional face feature point positioning method based on noise reduction self-coding network
  • Three-dimensional face feature point positioning method based on noise reduction self-coding network
  • Three-dimensional face feature point positioning method based on noise reduction self-coding network

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Embodiment Construction

[0082] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0083] Referring to the accompanying drawings, specific embodiments of the present invention will be described in more detail below. The programming implementation tool uses MATLAB R2015b and VS2015 to conduct experiments on the face point cloud in the Bosphorus library and the FRGC v2.0 library respectively.

[0084] Concrete implementation steps of the present invention are as follows:

[0085] Step 1: Locate the nose tip coordinates from the face point cloud, cut...

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Abstract

The invention discloses a three-dimensional face feature point positioning method based on a noise reduction self-coding network. The method comprises the following implementation steps: extracting anose tip point coordinate from a messy face point cloud; extracting a human face region through the position of the nose tip point and preprocessing the human face region; and training a face model, carrying out manual region segmentation on the face by the model, and dividing the to-be-detected face into a plurality of regions according to the segmented face and a rigid matching algorithm; performing shielding detection on each region, and converting the shielding degree into coefficient representation; training a noise reduction auto-encoder for each divided shielding area, wherein each noise reduction auto-encoder outputs a feature point positioning result; fusing the positioning results of the plurality of feature points through the shielding coefficient to obtain a final result, and finishing the whole fixed point algorithm.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for locating three-dimensional facial feature points based on a noise reduction autoencoder network. Background technique [0002] In research fields such as face verification, face recognition, and facial expression recognition, accurate coordinates of face feature points are needed to extract features more conveniently. How to accurately and automatically locate facial feature points has received more and more attention and research from scholars. Due to the influence of light, the location of feature points based on two-dimensional images still has a large error in complex situations. Therefore, face feature point localization on 3D point cloud has been widely studied and applied. The 3D point cloud describes the rich information of the human face surface, including curvature changes and depth changes. However, in actual situations, there are of...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/161G06V40/168G06V40/172Y02T90/00
Inventor 盖绍彦汪亮达飞鹏
Owner SOUTHEAST UNIV
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