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Face authentication method and face authentication device

A face verification, to-be-verified technology, applied in the field of computer vision, can solve the problems of large fluctuation of recognition rate, large environmental impact, low discrimination rate of face verification, etc., to improve the verification rate and accuracy, and good robustness. Effect

Inactive Publication Date: 2017-01-25
LETV HLDG BEIJING CO LTD +1
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AI Technical Summary

Problems solved by technology

However, the inventors have found that at least the following problems exist in the prior art: although the traditional feature comparison speed is fast, it is greatly affected by the environment, and the recognition rate fluctuates greatly on the whole; the recognition rate of the neural network-based method is relatively high. It has a good tolerance for environmental changes. Now, the method of extracting features based on a single deep neural network model and then comparing them is commonly used. However, it is necessary to calculate the features of the image twice during recognition, which is time-consuming. In essence, it is also a This kind of feature extractor does not take into account the difference between the two images in face verification in a more targeted manner.
This makes it easy to be affected by the environment and the differences in the images to be verified in the process of face verification, resulting in low discrimination rate of face verification and insufficient accuracy of verification results.

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Embodiment Construction

[0023] In order to make the object, technical solution and advantages of the present invention clearer, various embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, those of ordinary skill in the art can understand that, in each implementation manner of the present invention, many technical details are provided for readers to better understand the present application. However, even without these technical details and various changes and modifications based on the following implementation modes, the technical solution claimed in this application can also be realized.

[0024] The first embodiment of the present invention relates to a face verification method, the specific operation flow is as follows figure 1 shown.

[0025] In step 101, three face poses are selected, and a bilateral deep convolutional neural network model is preset.

[0026] Specifically, after selecting three face poses, it is necessa...

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Abstract

The invention relates to the vision field of a computer and discloses a face authentication method and a face authentication device. The face authentication method comprises the steps of selecting N face poses and presetting corresponding a bilateral depth convolution nerve network mode to each face pose, wherein N is natural number; respectively determining the face poses of two images to be verified; according to determined face poses, selecting the corresponding bilateral depth convolution nerve network mode; discriminating two images to be verified by the selected bilateral depth convolution nerve network mode; confirming whether the faces in two images to be verified are the same or not according to recognition results. By the face authentication method and the face authentication device, a problem that the current method and the current device have low discrimination rate and inaccurate recognition caused due to difference of complicated environment and authenticated images is solved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a face verification method and a face verification device. Background technique [0002] With the development of computer vision technology and image recognition technology, the face verification method of verifying whether it is the same face by identifying the color and area has been widely used in various scenarios such as real-name verification, user login, and transaction. [0003] At present, there are many face verification methods, which can be roughly divided into two categories, one is based on traditional feature extraction and comparison methods; the other is based on deep learning methods. However, the inventors found that there are at least the following problems in the prior art: although the traditional feature comparison speed is fast, it is greatly affected by the environment, and the recognition rate fluctuates greatly on the whole; the recognition rate of the ne...

Claims

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Application Information

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IPC IPC(8): G06F21/32G06K9/00
CPCG06F21/32G06V40/161
Inventor 公绪超刘阳魏伟白茂生
Owner LETV HLDG BEIJING CO LTD
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