Human face feature point locating method and apparatus

A technology of face features and positioning methods, applied in the computer field, can solve problems such as inaccurate positioning of face feature points, and achieve the effect of improving positioning accuracy and accuracy

Active Publication Date: 2018-01-19
VIVO MOBILE COMM CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide a method and device for locating facial feature points to solve the problem of inaccurate positioning of facial feature points under complex backgrounds and various postures in the prior art

Method used

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  • Human face feature point locating method and apparatus
  • Human face feature point locating method and apparatus
  • Human face feature point locating method and apparatus

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Experimental program
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Effect test

Embodiment 1

[0050] refer to figure 1 , which shows a flow chart of Embodiment 1 of a method for locating facial feature points according to the present invention, which may specifically include the following steps:

[0051] Step 101, obtaining a face image;

[0052] Embodiments of the present invention are applicable to general-purpose or special-purpose computing system environments or configurations. Examples: Personal computers, server computers, handheld or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, minicomputers, mainframe computers, including any of the above Or the distributed computing environment of the device and so on.

[0053] In the embodiment of the present invention, firstly, the face image to be positioned may be acquired. Specifically, the face image may be obtained in any manner, for example, a face image of any format and size may be obtained through network download, ...

Embodiment 2

[0078] In practical applications, there will be a problem of information loss in the process of information transmission in the convolutional layer, and as the number of layers in the deep neural network increases, the training error will also increase, and the network will be less likely to converge. In order to solve the above problems, the embodiment of the present invention adopts a deep convolutional neural network based on residual learning to solve the problem of information loss in the convolutional layer and increase the convergence speed of the network training process.

[0079] In an optional embodiment of the present invention, the deformable convolutional neural network may specifically be a deep convolutional neural network based on residual learning; the deformable convolutional neural network includes at least one convolutional layer, and A layer-hopping connection is set between the at least one convolutional layer; the acquisition of the feature map correspond...

Embodiment 3

[0113] This embodiment describes in detail the training process of the network model of the present invention on the basis of the foregoing embodiments. refer to figure 2 , showing a flow chart of an embodiment of a method for training the network model of the present invention, which may specifically include:

[0114] Step 201, collecting face sample images under various backgrounds and postures;

[0115] Specifically, face images under various backgrounds and poses can be collected from network downloads, camera photography, and the like. For example, the same face can be shot under different lighting conditions, or it can be shot from different angles to obtain face images with different backgrounds and postures, such as strong light, low light, front, side, head down, head up, wearing glasses , not wearing glasses, etc.

[0116] Step 202, marking the location information of the face feature points in the face sample image;

[0117] Specifically, the position of the fa...

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PUM

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Abstract

Embodiments of the invention provide a human face feature point locating method and apparatus. The method comprises the steps of obtaining a human face image; and processing the human face image through a deep neural network to obtain position information of human face feature points in the human face image, wherein the deep neural network is a network model obtained by training according to humanface samples, and the human face samples comprise human face sample images under multiple backgrounds and poses and the position information of the human face feature points corresponding to the human face sample images. Through the method and the apparatus, the accuracy of locating the human face feature points under complex backgrounds and multiple poses can be improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for locating facial feature points. Background technique [0002] Facial feature point positioning refers to the recognition of images based on face detection technology to determine the location information of key facial feature points such as eyes, nose, mouth, and facial contours. Face feature point location technology is a key issue in the fields of face recognition, graphics and computer vision, and plays an important role. [0003] At present, facial feature point location methods mainly include deformable template method, point distribution model method, graphical model method, cascaded shape regression method, etc. Among them, the cascade shape regression method has higher positioning accuracy and speed, and is widely used. [0004] However, the cascade shape regression method is very dependent on the accuracy of the initial feature points. In prac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04
Inventor 黄朝露
Owner VIVO MOBILE COMM CO LTD
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