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Model training method and device

A technology for model training and model recognition, applied in the field of image processing, can solve problems such as limited number of samples and inaccurate face recognition models, and achieve the effect of improving accuracy

Inactive Publication Date: 2017-03-15
BEIJING XIAOMI MOBILE SOFTWARE CO LTD
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AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the trained face recognition model is not accurate enough due to the limited number of samples, the present disclosure provides a model training method and device

Method used

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Embodiment Construction

[0052] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

[0053] The model training method provided by various embodiments of the present disclosure can be used in terminal devices such as portable computers, desktop computers, smart phones, and tablet computers, and the model training method is implemented by a processor in the terminal device. Optionally, the processor is a GPU (Graphics Processing Unit, graphics processing unit) or a CPU (...

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Abstract

The invention discloses a model training method and device, and belongs to the field of image processing. The method includes selecting a training sample set from a test sample set, training to obtain an identification model according to samples included in the training sample set, the training sample set being a subset of the test sample set, the training sample set including positive samples and negative samples, the number of the negative samples in the training sample set being smaller than that of the negative samples in the test sample set, identifying samples included in the test sample set according to the identification model, and updating the identification model according to samples that are wrongly identified; the problem that since the samples are limited in number, a face identification model obtained by training is not accurate enough is solved; after the identification model is obtained by training a limited number of samples, the identification model is updated again according to the samples that are wrongly identified, the identification model can be constantly optimized, and thus the accuracy is improved when the identification model is used for identification.

Description

technical field [0001] The present disclosure relates to the field of image processing, and in particular to a model training method and device. Background technique [0002] Face recognition technology is widely used in different fields such as camera autofocus, access control systems, and identity recognition. The current face recognition technology usually recognizes face images based on pre-trained face recognition models. [0003] When training a face recognition model, it is necessary to extract a training sample set from one or more preset images, which usually include face images and non-face images, so the extracted training sample set usually includes several normal samples and several negative samples. The positive samples are the face images contained in the preset image, and the negative samples are the non-face images contained in the preset image. The face recognition model is obtained by training each sample included in the training sample set . Due to the ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/16G06F18/214
Inventor 万韶华杨松陈志军
Owner BEIJING XIAOMI MOBILE SOFTWARE CO LTD
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