Abnormal face recognition method, abnormal face recognition device, abnormal face recognition equipment and storage medium

A recognition method and face recognition technology, applied in the field of face recognition, can solve the problems of recognition and verification failure, abnormal face falling into error, difficult to extract by neural network, etc., to achieve the effect of accurate abnormal face recognition

Pending Publication Date: 2020-08-28
卓望数码技术(深圳)有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the recognition work is mainly done through face verification or face recognition. However, under many specific conditions such as poor lighting, occluded objects, expression changes (such as laughing, crying, etc.), and side faces, the neural network It is difficult to extract features similar to "standard faces", and abnormal faces fall into the wrong position in the feature space, resulting in failure of recognition and verification

Method used

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  • Abnormal face recognition method, abnormal face recognition device, abnormal face recognition equipment and storage medium
  • Abnormal face recognition method, abnormal face recognition device, abnormal face recognition equipment and storage medium
  • Abnormal face recognition method, abnormal face recognition device, abnormal face recognition equipment and storage medium

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Embodiment 1

[0055] refer to figure 1 , shows a schematic flowchart of a method for identifying abnormal faces in an embodiment of the present invention. It specifically includes steps:

[0056] S100: Obtain abnormal face data; in this embodiment, the abnormal face data is abnormal face data, wherein the abnormal conditions include but are not limited to: poor lighting, occlusion, deformation (laughing, crying), etc.

[0057] S200: Process the abnormal face data, obtain the training face data, and extract the first face feature in the abnormal face data training face data; in this step, through the deep face recognition with stronger discrimination ability The device VGGFace describes the face features, and finally obtains the first face feature. The first face feature is a high-dimensional deep feature, which strengthens the face features in the abnormal face data, thereby improving the quality of the abnormal face data. The ability to express feature data improves the accuracy of the n...

Embodiment 2

[0067] Such as figure 2 Shown, in one embodiment, provides such as figure 1 A detailed process step of step S200 in the illustrated embodiment, in this embodiment, step S200 includes but not limited to the following steps:

[0068] S210: Extract the intermediate face features in the normal face data, and use the intermediate face features to obtain high-dimensional depth features; in this step, the deep face recognizer VGGFace describes the intermediate face features in the abnormal face data, and obtains high-dimensional features depth features. In other embodiments, for example, the dlib model and the hamtam12 model can also be used to identify human features.

[0069] S220: Eliminate the noise in the high-dimensional depth features, and obtain the first intermediate face data. In the abnormal face data, due to various factors, the face features are partially missing and contain part of the noise. In some embodiments, the LLE projection is used to eliminate this. Part of...

Embodiment 3

[0074] refer to image 3 , embodiment 3 on the basis of embodiment 2, further provides the second intermediate face data to carry out dimensionality reduction, obtains the method for training face data, this method comprises the steps:

[0075] S241: Obtain the number of neighborhoods in the second intermediate face data; in this step, firstly, it is necessary to determine the size of the neighborhood, that is, how many field samples are needed to linearly represent the second intermediate face data, and the number of field samples is K. In a specific embodiment, the k nearest neighbors of a sample may be selected by a distance metric such as Euclidean distance.

[0076] S242: Determine the linear relationship between the neighborhoods; in this step, determining the linear relationship of the second intermediate face data means determining the second intermediate face data X i and the linear relationship between the k nearest neighbors, that is, to find the weight coefficien...

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Abstract

The embodiment of the invention discloses an abnormal face recognition method, an abnormal face recognition device, abnormal face recognition equipment and a storage medium, and the method comprises the steps: obtaining abnormal face data; processing the abnormal face data to obtain training face data, and extracting a first face feature in the training face data; obtaining recognized face data, and obtaining a second face feature in the recognized face data; embedding the second face feature into the first face feature, and constructing a recognition face data set; and training the recognizedface data set to realize abnormal face recognition. According to the embodiment of the invention, data processing is carried out through abnormal face data (abnormal face data), the face features inthe abnormal face data are enhanced, the feature data (recognized face data) of the normal face are embedded into the abnormal face feature data set through the LLE algorithm, deep training and modeloutput of the network are completed, and accurate abnormal face recognition can be achieved.

Description

technical field [0001] The present invention relates to the technical field of face recognition, in particular to a method for recognizing abnormal faces, a recognition device, a recognition device and a storage medium. Background technique [0002] With the development of artificial intelligence technology, face recognition technology is becoming more and more mature, and its application scope is becoming wider and wider. In personal mobile phones, security, airports and other scenarios, face recognition technology is widely used for personnel tracking and identity authentication. All walks of life have brought great convenience and improved the efficiency of social operations. [0003] At present, the recognition work is mainly done through face verification or face recognition. However, under many specific conditions such as poor lighting, occluded objects, expression changes (such as laughing, crying, etc.), and side faces, the neural network It is difficult to extract ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/168G06F18/214
Inventor 宋海鹏
Owner 卓望数码技术(深圳)有限公司
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