CN113887561B - Face recognition method, device, medium and product based on data analysis - Google Patents

Face recognition method, device, medium and product based on data analysis Download PDF

Info

Publication number
CN113887561B
CN113887561B CN202111032903.4A CN202111032903A CN113887561B CN 113887561 B CN113887561 B CN 113887561B CN 202111032903 A CN202111032903 A CN 202111032903A CN 113887561 B CN113887561 B CN 113887561B
Authority
CN
China
Prior art keywords
face recognition
data
label
learning rate
calculating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202111032903.4A
Other languages
Chinese (zh)
Other versions
CN113887561A (en
Inventor
杨政华
吴志伟
杨海军
黄振杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Lvan Industry And Commerce Co ltd
Original Assignee
Guangdong Lvan Industry And Commerce Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Lvan Industry And Commerce Co ltd filed Critical Guangdong Lvan Industry And Commerce Co ltd
Priority to CN202111032903.4A priority Critical patent/CN113887561B/en
Publication of CN113887561A publication Critical patent/CN113887561A/en
Application granted granted Critical
Publication of CN113887561B publication Critical patent/CN113887561B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Evolutionary Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)

Abstract

The invention provides a face recognition method based on data analysis, which comprises the following steps: analyzing the distribution of the data set by adopting relative entropy to obtain the distribution degree of the long tail of the face training data set; calculating a smoothing coefficient through an inverse correlation relation between the data long tail distribution degree and the smoothing coefficient; calculating the algorithm learning rate under the label smoothness through the learning rate and the smoothness coefficient of the face recognition under the condition of not using the label smoothness; calculating a cross entropy loss function by taking the smoothing coefficient and the learning rate as model hyper-parameters; and updating the weight parameters of the neural network through a cross entropy loss function, and identifying the face image based on the trained neural network model. The method quantitatively calculates the long-tail distribution degree of the training data based on the relative entropy, avoids the long-tail distribution of qualitative description data, and cannot distinguish the difference between different data sets; the design of the label smoothing parameters improves the face recognition performance, the design learning rate fully excavates positive and negative sample data information in the training process, and the model is guaranteed to be rapidly and fully learned.

Description

Face recognition method, device, medium and product based on data analysis
Technical Field
The invention relates to the technical field of face recognition, in particular to a face recognition method, device, medium and product based on data analysis.
Background
The main objective of the face recognition algorithm is to map low-dimensional input face images into high-dimensional features, the similarity of the face features of a single ID is high, and the similarity of the face features of different IDs is low, namely the face recognition algorithm is compact in class and dispersed among classes. In order to achieve the goal, a mainstream face recognition algorithm is designed aiming at a loss function and assisted by training strategies such as label smoothing, learning rate attenuation and the like, and hyper-parameters related to a training process are generally set based on experience and can obtain an optimal value only by adjusting parameters for many times. The face recognition algorithm needs a larger training data set, and meanwhile, the uniform data set can obviously improve the performance of the algorithm, but the current algorithm does not fully mine the relation between the data set and the hyper-parameters so as to achieve the aim of fully utilizing the existing data.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a face recognition method based on data analysis, which solves the problem that a face recognition algorithm does not fully mine the relation between a data set and a hyper-parameter so as to achieve the aim of fully utilizing the existing data.
The invention provides a face recognition method based on data analysis, which comprises the following steps:
calculating the long tail distribution degree, and analyzing the distribution of the data set by adopting relative entropy to obtain the long tail distribution degree of the face training data set;
calculating a label smoothing parameter, and calculating a smoothing coefficient through an inverse correlation relation between the data long tail distribution degree and the smoothing coefficient;
calculating a learning rate, namely calculating an algorithm learning rate under label smoothness by using the learning rate of the face recognition under the condition of not using label smoothness and the smoothing coefficient;
calculating a cross entropy loss function, and calculating the cross entropy loss function by taking the smoothing coefficient and the learning rate as model hyper-parameters;
and face recognition, namely updating weight parameters of the neural network through a cross entropy loss function, and recognizing a face image based on the trained neural network model.
Further, in the step of calculating the long-tail distribution degree, the long-tail distribution degree of the data is expressed as:
Figure BDA0003246062960000021
Figure BDA0003246062960000022
Figure BDA0003246062960000023
wherein D is KL(data) The distribution degree of the long tail of the face training data set is represented, the relative entropy of the data set which is uniformly distributed in an ideal state and the actual probability distribution of the current data set is represented, and S is { S ═ S 0 ,s 1 ,s 2 …,s n Is a face recognition training set, s i Number of face images per ID, n number of data set ID, q i True probability distribution for ith personal face ID with respect to data set, p i Is a uniform distribution with respect to n.
Further, the inverse correlation relationship between the data long-tail distribution degree and the smoothing coefficient is
Figure BDA0003246062960000024
Where ε is a smoothing coefficient designed based on data analysis.
Further, the algorithm learning rate calculation formula under the label smoothing is as follows
Figure BDA0003246062960000025
Wherein, lr norm Science representing face recognition without tag smoothingLearning rate, lr label_smooth Representing the algorithm learning rate under label smoothing.
Further, the cross entropy loss function is calculated by the formula
Figure BDA0003246062960000031
Wherein x is class Representing the current positive sample, y class Is the cross entropy loss value.
An electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing a method of face recognition based on data analysis.
A computer-readable storage medium, on which a computer program is stored, which computer program is executed by a processor for a method of face recognition based on data analysis.
A computer program product comprising a computer program/instructions which, when executed by a processor, implement a method of face recognition based on data analysis.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a face recognition method based on data analysis, which guides hyper-parameter design by quantitatively analyzing the long tail distribution information of training data. Firstly, the long-tail distribution degree of training data is quantitatively calculated based on relative entropy, so that the long-tail distribution of qualitative description data is avoided, and the difference between different data sets cannot be distinguished; label smoothing parameters are designed quantitatively based on the long-tail distribution degree, and the face recognition performance is improved; and the learning rate is designed based on the label smoothing coefficient, positive and negative sample data information in the training process is fully mined, and the model is ensured to be rapidly and fully learned. The method solves the problem that the face recognition algorithm does not fully mine the relation between the data set and the hyper-parameters so as to achieve the aim of fully utilizing the existing data.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a face recognition method based on data analysis according to the present invention;
FIG. 2 is a flowchart of a hyperparameter solution process according to an embodiment of the invention;
fig. 3 is a flowchart of a face recognition training process according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
A face recognition method based on data analysis, as shown in fig. 1-3, includes the following steps:
and calculating the distribution degree of long tails, wherein the data is the basis of a deep learning face recognition algorithm, and the magnitude and the distribution mode of the data determine the final performance of face recognition. In the actual acquisition process, images of a few IDs can be acquired in a large quantity through the Internet, and most IDs can only acquire limited data, so that the current public data set presents obvious long-tail distribution. Practice proves that the uniformly distributed data is more friendly to face recognition and better in algorithm performance, but the long tail distribution phenomenon is not quantitatively analyzed at present so as to guide the design of the hyper-parameters. In the embodiment, relative entropy is adopted to analyze data set distribution, the long tail distribution phenomenon of a training data set is quantitatively analyzed at first, and quantitative indexes of the long tail distribution of the data are given for guiding the design of a label smoothing coefficient and a learning rate hyper-parameter in the training process. The specific method comprises the following steps:
the definition of relative entropy (KL divergence) is
Figure BDA0003246062960000041
Introducing KL divergence to quantitatively express the long tail distribution phenomenon of data, and assuming that a face recognition training set is S ═ S 0 ,s 1 ,s 2 …,s n In which s is i Defining probability distribution p in divergence of long tail distribution KL for the number of face images of each ID i Comprises the following steps:
Figure BDA0003246062960000051
where n is the number of IDs of the data set, defining q i Is the true probability distribution for the data set:
Figure BDA0003246062960000052
the long tail distribution of the data is expressed as:
Figure BDA0003246062960000053
wherein D is KL(data) The distribution degree of the long tail of the face training data set is represented, and the distribution degree is the relative entropy of the data set in an ideal state, wherein the data set is uniformly distributed and the actual probability distribution of the current data set.
Calculating a label smoothing parameter, wherein label smoothing is an important skill for improving the performance of a face recognition model, and a label smoothing coefficient is generally designed based on experience, but has different values under different data distributions. The more serious the long tail distribution phenomenon of the training data is, the smaller the label smoothing coefficient is, so as to promote the learning of positive samples and reduce the learning of negative samples; and under the condition that the long tail distribution phenomenon is not serious, the label smoothing coefficient can be increased, and negative sample learning is promoted. The distribution degree of the long tail of the data is inversely proportional to the smooth coefficient epsilon, when the distribution degree of the long tail of the data is 0.52 and the smooth coefficient is 0.1, the performance of the face recognition model is optimal, and the relation between the smooth coefficient and the distribution degree of the long tail is defined as follows:
Figure BDA0003246062960000054
the quantitative design of the smoothing coefficient epsilon can reduce the parameter adjusting times of algorithm personnel and save the training time and hardware resources of the face recognition algorithm.
Calculating a learning rate, wherein under the same learning rate, the update amplitude of the weight parameter of the label smoothing model is inconsistent with that of the weight parameter of the label smoothing model without the label smoothing model, and the update amplitude of the weight parameter of the positive sample under the condition of label smoothing is obviously smaller than that of the weight parameter without the label smoothing model, and analyzing as follows:
Figure BDA0003246062960000061
Figure BDA0003246062960000062
wherein, y norm Representing an untagged smooth parameter gradient, y label_smooth Indicating the parameter gradient after adding the label and smoothing. Each time the parameter is updated, the positive sample parameter update without tagging smoothing is greater in magnitude than the tagging smoothing, and the magnitude is ε. Therefore, in order to guarantee equal learning of the positive sample, the algorithm learning rate under the label smoothness is calculated by the learning rate and the smoothness coefficient of the face recognition under the condition of not using the label smoothness. The learning rate and the smoothing parameters under the label smoothing are designed as follows:
Figure BDA0003246062960000063
lr norm indicating the learning rate, lr, of face recognition without label smoothing label_smooth Representing the algorithm learning rate under label smoothing. By adjusting the learning rateThe parameters can further mine the advantage of label smoothness, and the learning process of the model can be accelerated due to the larger learning rate.
And calculating a cross entropy loss function, and calculating the cross entropy loss function by taking the smoothing coefficient and the learning rate as model hyper-parameters. The loss function under label smoothing is:
Figure BDA0003246062960000064
wherein x is class Representing the current positive sample, y class Is the cross entropy loss value.
And face recognition, namely updating weight parameters of the neural network through a cross entropy loss function, and recognizing a face image based on the trained neural network model.
An electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing a method of face recognition based on data analysis.
A computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor for a method of face recognition based on data analysis.
A computer program product comprising a computer program/instructions which, when executed by a processor, implement a method of face recognition based on data analysis.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can readily practice the invention as shown and described in the drawings and detailed description herein; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.

Claims (7)

1. A face recognition method based on data analysis is characterized by comprising the following steps:
calculating the long tail distribution degree, and analyzing the distribution of the data set by adopting relative entropy to obtain the long tail distribution degree of the face training data set;
calculating a label smoothing parameter, and calculating a smoothing coefficient through an inverse correlation relation between the data long tail distribution degree and the smoothing coefficient;
calculating a learning rate, namely calculating an algorithm learning rate under label smoothness by using the learning rate of the face recognition under the condition of not using label smoothness and the smoothing coefficient;
calculating a cross entropy loss function, and taking the smoothing coefficient and the algorithm learning rate under the label smoothing as a model hyper-parameter to calculate the cross entropy loss function;
and face recognition, namely updating weight parameters of the neural network through a cross entropy loss function, and recognizing a face image based on the trained neural network model.
2. A method of face recognition based on data analysis as claimed in claim 1, characterized in that: in the step of calculating the long tail distribution degree, the long tail distribution degree of the data is expressed as:
Figure FDA0003703658950000011
Figure FDA0003703658950000012
Figure FDA0003703658950000013
wherein D is KL(data) The distribution degree of the long tail of the face training data set is represented, and the data set is uniform in an ideal stateRelative entropy of distribution to actual probability distribution of current data set, S ═ S 0 ,s 1 ,s 2 …,s n Is the face recognition training set, s i Number of face images per ID, n number of data set ID, q i True probability distribution, p, for the ith personal face ID with respect to the data set i Is a uniform distribution with respect to n.
3. A method of face recognition based on data analysis as claimed in claim 2, characterized in that: the inverse correlation relation between the data long tail distribution degree and the smooth coefficient is
Figure FDA0003703658950000021
Where ε is a smoothing coefficient designed based on data analysis.
4. A face recognition method based on data analysis as claimed in claim 3, characterized in that: the algorithm learning rate calculation formula under the label smoothness is
Figure FDA0003703658950000022
Wherein, lr norm Indicating the learning rate, lr, of face recognition without label smoothing label_smooth Representing the algorithm learning rate under label smoothing.
5. A face recognition method based on data analysis as claimed in claim 3, characterized in that: the cross entropy loss function calculation formula is
Figure FDA0003703658950000023
Wherein x is class Representing the current positive sample, y class Is the cross entropy loss value.
6. An electronic device, characterized by comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for carrying out the method according to any one of claims 1-5.
7. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor for performing the method according to any of claims 1-5.
CN202111032903.4A 2021-09-03 2021-09-03 Face recognition method, device, medium and product based on data analysis Active CN113887561B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111032903.4A CN113887561B (en) 2021-09-03 2021-09-03 Face recognition method, device, medium and product based on data analysis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111032903.4A CN113887561B (en) 2021-09-03 2021-09-03 Face recognition method, device, medium and product based on data analysis

Publications (2)

Publication Number Publication Date
CN113887561A CN113887561A (en) 2022-01-04
CN113887561B true CN113887561B (en) 2022-08-09

Family

ID=79012346

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111032903.4A Active CN113887561B (en) 2021-09-03 2021-09-03 Face recognition method, device, medium and product based on data analysis

Country Status (1)

Country Link
CN (1) CN113887561B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107330355A (en) * 2017-05-11 2017-11-07 中山大学 A kind of depth pedestrian based on positive sample Constraints of Equilibrium identification method again
CN107341497A (en) * 2016-11-11 2017-11-10 东北大学 The unbalanced weighting data streams Ensemble classifier Forecasting Methodology of sampling is risen with reference to selectivity
WO2020073951A1 (en) * 2018-10-10 2020-04-16 腾讯科技（深圳）有限公司 Method and apparatus for training image recognition model, network device, and storage medium
CN112381756A (en) * 2020-09-30 2021-02-19 广东省科学院智能制造研究所 Hyperspectral data analysis method and system based on block smoothing neural network
CN112487899A (en) * 2020-11-19 2021-03-12 武汉高德飞行器科技有限公司 Target identification method and system based on unmanned aerial vehicle, storage medium and electronic equipment
CN112632319A (en) * 2020-12-22 2021-04-09 天津大学 Method for improving overall classification accuracy of long-tail distributed speech based on transfer learning

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111597907B (en) * 2020-04-21 2022-08-02 广东工业大学 Anti-noise meta-learning-based face recognition method and system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107341497A (en) * 2016-11-11 2017-11-10 东北大学 The unbalanced weighting data streams Ensemble classifier Forecasting Methodology of sampling is risen with reference to selectivity
CN107330355A (en) * 2017-05-11 2017-11-07 中山大学 A kind of depth pedestrian based on positive sample Constraints of Equilibrium identification method again
WO2020073951A1 (en) * 2018-10-10 2020-04-16 腾讯科技（深圳）有限公司 Method and apparatus for training image recognition model, network device, and storage medium
CN112381756A (en) * 2020-09-30 2021-02-19 广东省科学院智能制造研究所 Hyperspectral data analysis method and system based on block smoothing neural network
CN112487899A (en) * 2020-11-19 2021-03-12 武汉高德飞行器科技有限公司 Target identification method and system based on unmanned aerial vehicle, storage medium and electronic equipment
CN112632319A (en) * 2020-12-22 2021-04-09 天津大学 Method for improving overall classification accuracy of long-tail distributed speech based on transfer learning

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Improving Calibration for Long-Tailed Recognition;Zhisheng Zhong et al.;《arXiv:2104.00466v1》;20210401;第1-15页 *
IS LABEL SMOOTHING TRULY INCOMPATIBLE WITH KNOWLEDGE DISTILLATION: AN EMPIRICAL STUDY;Zhiqiang Shen et al.;《arXiv:2104.00676v1》;20210401;第1-17页 *
Range Loss for Deep Face Recognition with Long-Tailed Training Data;Xiao Zhang et al.;《2017 IEEE International Conference on Computer Vision》;20171231;第5419-5428页 *
基于长尾分布数据集的目标检测算法研究;张夕萌;《中国优秀博硕士学位论文全文数据库（硕士） 信息科技辑》;20210615;I138-240 *
目标检测技术在无人机航拍图像中的应用;贺文瑞;《中国优秀博硕士学位论文全文数据库（硕士） 信息科技辑》;20210415;I138-720 *

Also Published As

Publication number Publication date
CN113887561A (en) 2022-01-04

Similar Documents

Publication Publication Date Title
CN109697289A (en) It is a kind of improved for naming the Active Learning Method of Entity recognition
WO2021174827A1 (en) Text generation method and appartus, computer device and readable storage medium
CN108804577B (en) Method for estimating interest degree of information tag
CN106156805A (en) A kind of classifier training method of sample label missing data
CN113469186A (en) Cross-domain migration image segmentation method based on small amount of point labels
CN115131618A (en) Semi-supervised image classification method based on causal reasoning
CN111753918A (en) Image recognition model for eliminating sex bias based on counterstudy and application
CN112990480A (en) Method and device for building model, electronic equipment and storage medium
CN111191722B (en) Method and device for training prediction model through computer
CN113177587A (en) Generalized zero sample target classification method based on active learning and variational self-encoder
CN113887561B (en) Face recognition method, device, medium and product based on data analysis
CN112801162B (en) Adaptive soft label regularization method based on image attribute prior
US20230342601A1 (en) Evaluating input data using a deep learning algorithm
Passalis et al. Adaptive normalization for forecasting limit order book data using convolutional neural networks
CN116090449B (en) Entity relation extraction method and system for quality problem analysis report
CN114120367B (en) Pedestrian re-recognition method and system based on circle loss measurement under meta-learning framework
CN114444654A (en) NAS-oriented training-free neural network performance evaluation method, device and equipment
CN110827919A (en) Dimension reduction method applied to gene expression profile data
CN117932073B (en) Weak supervision text classification method and system based on prompt engineering
CN118070775B (en) Performance evaluation method and device of abstract generation model and computer equipment
CN114036948B (en) Named entity identification method based on uncertainty quantification
CN104063591B (en) One-dimensional range profile identification method for non-library target based unified model
CN115565611B (en) Biological regression prediction method, device, equipment and storage medium
CN114385805B (en) Small sample learning method for improving adaptability of deep text matching model
CN117436449B (en) Crowd-sourced named entity recognition model and system based on multi-source domain adaptation and reinforcement learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant