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Training method combining face recognition data equalization and enhancement

A face recognition and training method technology, applied in the field of image classification based on deep learning, can solve the problems of data and real scene differences, data imbalance, etc., and achieve low loss convergence value, high balance, and fast training convergence Effect

Active Publication Date: 2020-01-14
CHENGDU KOALA URAN TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problems of unbalanced data and differences between data and real scenes in the prior art, the present invention proposes a training scheme combining face recognition data equalization and enhancement. Through online and offline data enhancement and sampling, the human Data balance and enhancement for face recognition

Method used

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  • Training method combining face recognition data equalization and enhancement
  • Training method combining face recognition data equalization and enhancement
  • Training method combining face recognition data equalization and enhancement

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Embodiment 1

[0043] A training method combining face recognition data equalization and enhancement, such as figure 1 shown, including the following steps:

[0044] S1. Generate a training pre-model using the photos contained in all the cluster label ids of the dataset;

[0045] S2. Screen out the enhanced sample picture bases in all clustering label ids of the data set; the enhanced sample picture bases are pictures whose quality meets certain conditions in each clustering label id;

[0046] S3. Perform offline enhancement on the enhanced sample image base, and filter out the similarity between each cluster label id in the enhanced sample image base and the cluster center of the cluster label id after offline enhancement in the similarity interval (a, b) n sample pictures between;

[0047] S4. Design a data equalization method to perform data equalization on the sample image;

[0048] S5. Perform online enhancement on the sample image after data equalization.

[0049] Working principle...

Embodiment 2

[0051] In order to realize the present invention better, further, combine figure 2 As shown, the S2 specifically includes the following steps:

[0052] S2.1. Use the pre-model generated by S1 to determine the class center of each class of cluster label id of the data set;

[0053] S2.2. According to the cluster center determined by the cluster label id of each category, select the pictures whose similarity with the cluster center is higher than the parameter t in the corresponding cluster label id.

[0054]Working principle: The class center of each cluster label id is determined through the pre-training model. For example, the glint dataset has 180,000 cluster label ids, that is, a matrix of 180000×512 is generated and saved. The calculation formula of each class center is as follows:

[0055] emb_i = model(img_i)

[0056] mid_emb = (emb_0 + emb_1 + ....emb_n) / n

[0057] mid_emb.shape: 1×512;

[0058] After the class center of each cluster label id is determined throug...

Embodiment 3

[0061] In order to better realize the present invention, further, the parameter t can be adjusted according to the size of the data set and actual requirements.

[0062] Working principle: Through the similarity parameter t, the pictures similar to the class center in the data set can be screened out, because the offline enhancement belongs to the category of large-scale enhancement, only the data near the class center is selected for selection, and the similarity parameter t is set for screening , can remove some outlayers and some data at the edge of the class; after many experiments in the present invention, the value of the parameter t is selected to be 0.65, but if the selected data sets are different, and the requirements for the accuracy of the experiment are different, The specific value of the similarity parameter t can be adjusted up and down independently.

[0063] Other parts of this embodiment are the same as those of Embodiment 1-2 above, so they will not be repe...

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Abstract

The invention discloses a training method combining face recognition data equalization and enhancement. The method comprises the steps: firstly training a pre-training model through a data set, and achieving a reproduction effect; an enhanced sample base is screened out through a pre-training model, the enhanced sample base is a picture with high quality in each type of clustering label id, and the accuracy of face recognition can be improved by screening out the enhanced sample base for recognition; according to the method, offline enhancement is carried out on an enhanced sample base, the offline enhancement is carried out by selecting rich and large-scale enhancement degrees, so that the enhanced sample base is more diversified, and a sample which has a relatively good enhancement effect and cannot be completely identified is screened out through similarity intervals (a, b), so that the picture cannot be enhanced; then, a data equalization method is designed to equalize the data, sothat the number of samples selected by each clustering label id is similar; and finally, carrying out primary offline enhancement on the equalized data. According to the invention, data equalizationand enhancement of face recognition are realized through the above steps.

Description

technical field [0001] The invention belongs to the field of image classification based on deep learning, and in particular relates to a training method combining equalization and enhancement of face recognition data. Background technique [0002] In recent years, with the rapid development of computer technology, automatic face recognition technology has been extensively researched and developed, and face recognition has become one of the most popular research topics in pattern recognition and image processing in the past 30 years. Face recognition is a biometric identification technology based on human facial feature information: use a camera or camera to collect images or video streams containing human faces, and automatically detect and track human faces in the images, and then detect A series of related technologies for face recognition. Face recognition is also commonly called portrait recognition and facial recognition. Face recognition technology can compare the ne...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06T3/00G06T5/00G06K9/00
CPCG06T2207/20024G06T2207/20032G06T2207/30201G06V40/172G06F18/23213G06F18/214G06T3/02G06T5/00G06T5/70
Inventor 张芮铭沈复民孔繁昊奚兴张艳明
Owner CHENGDU KOALA URAN TECH CO LTD
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