A static three-dimensional face in-vivo detection method based on deep learning

A living body detection and deep learning technology, applied in the field of face recognition, can solve the problems of high cost, large amount of calculation, and long time consumption, and achieve the effect of low cost, increased speed, and high precision

Active Publication Date: 2019-05-03
CHANGSHA XIAOGU TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In the above-mentioned related technologies, the data of two cameras is used to obtain 3D face feature points, which takes a long time and cannot achieve the purpose of real-time detection. At the same time, it relies on the accuracy of the human eye det

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  • A static three-dimensional face in-vivo detection method based on deep learning
  • A static three-dimensional face in-vivo detection method based on deep learning
  • A static three-dimensional face in-vivo detection method based on deep learning

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Embodiment 1

[0059] see figure 1 , the embodiment of the present invention provides a static three-dimensional human face detection method based on deep learning, the method specifically includes the following steps:

[0060] Step 101: Take a color image with a color camera, and take a depth image with a depth camera.

[0061] The embodiment of the present invention only uses two cameras, one color camera and one depth camera. Products applying the human face liveness detection provided by the embodiments of the present invention, such as bank ATM machines or household door locks, etc., only need to be equipped with these two cameras. The scene in the monitoring area is photographed by the color camera and the depth camera, and the color image and the depth image corresponding to the monitoring area are respectively obtained.

[0062] After the color image and the depth image are captured, the color image and the depth image are firstly registered to ensure that the same object is in the...

Embodiment 2

[0104] see Figure 7 , the embodiment of the present invention provides a deep learning-based static three-dimensional human face detection device, the device is used to implement the deep learning-based static three-dimensional human face detection method provided in the above-mentioned embodiment 1, the device includes:

[0105] The photographing module 20 is used for photographing a color image by a color camera, and photographing a depth image by a depth camera;

[0106] The vector obtaining module 21 is used to obtain the feature vector corresponding to the human face through the first convolutional neural network and the second convolutional neural network according to the color image and the depth image when a human face is detected in the color image;

[0107] The judging module 22 is configured to judge whether the human face is a living body through a pre-trained life detection classifier according to the feature vector corresponding to the human face.

[0108] Abov...

Embodiment 3

[0123] An embodiment of the present invention provides a static three-dimensional human face detection device based on deep learning, the device includes one or more processors, and a storage device; the storage device is used to store one or more programs; when the one or more When the program is loaded and executed by the one or more processors, the deep learning-based static three-dimensional human face detection method provided in the first embodiment above is realized.

[0124] In the embodiment of the present invention, a color image is captured by a color camera, and a depth image is captured by a depth camera; when a human face is detected in the color image, according to the color image and the depth image, the first convolution The neural network and the second convolutional neural network obtain the feature vector corresponding to the human face; according to the feature vector corresponding to the human face, it is judged whether the human face is a living body thro...

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Abstract

The invention discloses a static three-dimensional face in-vivo detection method based on deep learning, and the method comprises the steps: shooting a color image through a color camera, and shootinga depth image through a depth camera; when a face is detected in the color image, obtaining a feature vector corresponding to the face through a first convolutional neural network and a second convolutional neural network according to the color image and the depth image; and judging whether the face is a living body or not through a pre-trained living body detection classifier according to the feature vector corresponding to the face. Only two cameras are adopted, the characteristics of color images and depth images are combined, the technologies of deep learning, machine learning and the like are combined, the speed, the passing rate and the anti-counterfeiting rate of human face living body detection are greatly increased, the cost is low, and the precision is high.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a static three-dimensional human face detection method based on deep learning. Background technique [0002] With the continuous development of face recognition technology, many products have begun to use face recognition technology to verify user identities, such as bank ATM machines, unmanned stores and even home door locks. However, general face recognition technology cannot effectively detect whether the user is alive or not. Therefore, malicious people can pretend to be legitimate users by printing other people's photos or using mobile phones to shoot other people's videos, and deceive the face recognition system to achieve their malicious purposes. Therefore, face detection technology came into being. [0003] Currently, a face liveness detection technology is provided in the related art, which uses images captured by two cameras to obtain 3D face feat...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06F18/00
Inventor 陈俊逸
Owner CHANGSHA XIAOGU TECH CO LTD
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