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Living body face detection model training method and device, equipment and storage medium

A face detection and training method technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as unbalanced model prediction accuracy and substandard

Pending Publication Date: 2021-08-20
CHINA PING AN LIFE INSURANCE CO LTD
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AI Technical Summary

Problems solved by technology

[0004] Embodiments of the present invention provide a training method, device, computer equipment, and storage medium for a live face detection model to solve the technical problem that the prediction accuracy of the model is not up to standard when the binary classification model is trained through extremely unbalanced positive and negative samples

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  • Living body face detection model training method and device, equipment and storage medium
  • Living body face detection model training method and device, equipment and storage medium
  • Living body face detection model training method and device, equipment and storage medium

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Embodiment Construction

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0027] The training method of the live face detection model provided by this application can be applied in such as figure 1 An application environment in which the computer device can communicate with a server through a network. Wherein, the computer equipment can be but not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The server can be implemented by an independent server or ...

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Abstract

The invention discloses a living body face detection model training method, which is applied to the technical field of artificial intelligence and is used for solving the technical problem that the prediction precision of a model does not reach the standard when a binary classification model is trained through extremely unbalanced positive and negative samples. The method provided by the invention comprises the following steps: acquiring a face picture sample set carrying a label; inputting a normal face image sample and an abnormal face image sample into a living body face detection model to be trained to obtain an original prediction value; correcting the original predicted value of the abnormal face picture sample to obtain a corrected predicted value; taking the corrected predicted value as a predicted value of the abnormal face picture sample, taking an original predicted value as a predicted value of the normal face picture sample, and training the living body face detection model to be trained according to the category to which the corresponding face picture sample identified in the label actually belongs. When a loss function of the living body face detection model converges, the trained living body face detection model is obtained.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a training method, device, equipment and storage medium for a living human face detection model. Background technique [0002] In the anti-fraud risk control scenario, smart customer service scenarios include: counterfeit certificate anti-fraud, face detection, identification of high-risk customers / users based on customer historical data, big data anti-money laundering, etc. These scenarios are common in nuclear security risk control. Moreover, the above scenarios all contain extremely unbalanced sample characteristics. In many projects, the risk control target sample rate of some scenarios is even lower than 2%. The problems caused by such extremely unbalanced sample characteristics have greatly improved the model effect. Difficulties. [0003] Taking the scene of the live face detection model as an example, since the number of forged samples of live fa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/172G06F18/214
Inventor 喻晨曦
Owner CHINA PING AN LIFE INSURANCE CO LTD
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