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WO2018187951A1 - Procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau - Google Patents

Procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau Download PDF

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Publication number
WO2018187951A1
WO2018187951A1 PCT/CN2017/080175 CN2017080175W WO2018187951A1 WO 2018187951 A1 WO2018187951 A1 WO 2018187951A1 CN 2017080175 W CN2017080175 W CN 2017080175W WO 2018187951 A1 WO2018187951 A1 WO 2018187951A1
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WO
WIPO (PCT)
Prior art keywords
principal component
component analysis
kernel
method based
recognition method
Prior art date
Application number
PCT/CN2017/080175
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English (en)
Chinese (zh)
Inventor
邹霞
Original Assignee
邹霞
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 邹霞 filed Critical 邹霞
Priority to PCT/CN2017/080175 priority Critical patent/WO2018187951A1/fr
Publication of WO2018187951A1 publication Critical patent/WO2018187951A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition

Definitions

  • the invention relates to a face recognition method based on kernel principal component analysis, belonging to the field of biometric identification.
  • Face recognition is a computer technology that achieves the purpose of identity identification by analyzing human facial visual features.
  • the academic community gives a specific definition of face recognition in both broad and narrow sense.
  • Generalized face recognition includes face detection, face representation, face identification, face expression analysis, and physical classification.
  • Narrow face recognition is defined as a technology or system that enables identity verification, identity comparison, and identity lookup through facial features.
  • biometrics mainly come from the following aspects: face, retina, iris, palmprint, fingerprint, voice, body shape, habits, etc. Therefore, based on the above, research is focused on identifying faces, retinas, and irises.
  • the computer recognition technology of the corresponding features such as palm print, fingerprint, voice, body shape, keyboard stroke, signature, etc., has achieved important results.
  • the advantage of face recognition lies in its natural and friendly characteristics.
  • the so-called natural nature means that human beings also identify and confirm the identity of each other by observing and comparing human facial features.
  • speech recognition and body shape recognition also have natural characteristics, while humans or other creatures usually do not pass fingerprints.
  • Features such as iris distinguish individuals, so the above feature recognition does not have natural characteristics. Sign.
  • the so-called friendliness means that the identification method does not increase the psychological burden of the authenticated person due to special treatment, and thus it is easier to obtain direct and true feature information.
  • Fingerprint or iris recognition needs to use special techniques such as electronic pressure sensor or infrared to collect information.
  • the above special collection technology is easy to be discovered, which greatly increases the possibility that the authenticated person avoids identity identification and reduces the efficiency of identity authentication.
  • face recognition can directly obtain the face information of the authenticated person through simple image or video technology.
  • This information collection method is not easy to be perceived, which increases the authenticity and reliability of the information.
  • the structure of the same type of face has a high similarity. This feature can be used for face localization, but it greatly increases the difficulty of using individual facial features to identify individuals.
  • the shape of the face is very unstable. Even at different viewing angles, the image features of the face are significantly different, and the face recognition technology is added. The complexity of the application.
  • an object of the present invention is to provide a face recognition method based on kernel principal component analysis, comprising:
  • Step 1 Calculate a kernel matrix for a given M training set data X[x 1 , x 2 , . . . , x M ];
  • Step 2 Construct a centralization matrix H to solve the characteristic equation
  • Step three calculating a vector
  • Step 4 Extract the principal component, form the feature subspace, and obtain the principal component analysis of the face data. After the retained sample data set Y;
  • Step 5 For the test data set X', project it into the feature subspace of the training set to obtain a test data set Y' after feature extraction;
  • Step 6 Classify the sample Y' by the nearest neighbor classifier.
  • the above step 4 is to extract the first k total contribution rates of 90% or more.
  • the face recognition method based on kernel principal component analysis provided by the invention can greatly shorten the recognition time, and the core method is used to make up for the fact that the principal component analysis method and the linear discriminant analysis method cannot utilize the data in the middle.
  • the shortcoming of linear information Compared with the prior art, the face recognition method based on kernel principal component analysis provided by the invention can greatly shorten the recognition time, and the core method is used to make up for the fact that the principal component analysis method and the linear discriminant analysis method cannot utilize the data in the middle. The shortcoming of linear information.
  • the present invention provides a face recognition method based on kernel principal component analysis, and the present invention will be further described in detail in the following examples in order to clarify and clarify the objects, technical solutions and effects of the present invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
  • the face recognition method based on kernel principal component analysis maps sample data from a low-dimensional space to a high-dimensional space by a kernel method, so that the PCA algorithm has the processing capability for nonlinear data.
  • the principal component contains most of the useful information with informational value.
  • the principal component analysis is to find the eigenvalues and eigenvectors of the matrix for the covariance matrix C:
  • test data set X' it is projected into the feature subspace of the training set to obtain the feature extracted test data set Y'.
  • the face recognition method based on kernel principal component analysis provided by the invention can greatly shorten the recognition time, and the core method is used to make up for the fact that the principal component analysis method and the linear discriminant analysis method cannot utilize the data in the middle.
  • the shortcoming of linear information Compared with the prior art, the face recognition method based on kernel principal component analysis provided by the invention can greatly shorten the recognition time, and the core method is used to make up for the fact that the principal component analysis method and the linear discriminant analysis method cannot utilize the data in the middle. The shortcoming of linear information.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau. Le procédé comprend les étapes consistant à : calculer une matrice de noyau pour M éléments de données d'ensemble d'apprentissage donné X[X1, X2, … , XM]; construire une matrice de centrage H et résoudre une équation caractéristique ; calculer un vecteur ; extraire un composant principal pour former un sous-espace caractéristique et obtenir un ensemble de données d'échantillon Y conservé après l'analyse de composante principale de données faciales ; projeter un ensemble de données de test X' au sous-espace caractéristique de l'ensemble d'apprentissage pour obtenir un ensemble de données de test Z' après extraction de caractéristiques ; et classifier et reconnaître l'échantillon Z' au moyen d'un classificateur voisin le plus proche. Le procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau peut raccourcir de manière significative le temps de reconnaissance. L'application d'un procédé de noyau peut résoudre complètement le défaut selon lequel des informations non linéaires dans des données ne peuvent pas être utilisées dans le procédé d'analyse de composant principal et le procédé d'analyse discriminante linéaire.
PCT/CN2017/080175 2017-04-12 2017-04-12 Procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau WO2018187951A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/080175 WO2018187951A1 (fr) 2017-04-12 2017-04-12 Procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/080175 WO2018187951A1 (fr) 2017-04-12 2017-04-12 Procédé de reconnaissance faciale basé sur une analyse de composant principal de noyau

Publications (1)

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WO2018187951A1 true WO2018187951A1 (fr) 2018-10-18

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111914886A (zh) * 2020-06-13 2020-11-10 宁波大学 一种基于在线简略核学习的非线性化工过程监测方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1599917A (zh) * 2001-12-03 2005-03-23 本田技研工业株式会社 使用Kernel Fisherfaces的面部识别
US20130142399A1 (en) * 2011-12-04 2013-06-06 King Saud University Face recognition using multilayered discriminant analysis
CN104361337A (zh) * 2014-09-10 2015-02-18 苏州工业职业技术学院 计算和存储空间受限下的稀疏核主成分分析方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1599917A (zh) * 2001-12-03 2005-03-23 本田技研工业株式会社 使用Kernel Fisherfaces的面部识别
US20130142399A1 (en) * 2011-12-04 2013-06-06 King Saud University Face recognition using multilayered discriminant analysis
CN104361337A (zh) * 2014-09-10 2015-02-18 苏州工业职业技术学院 计算和存储空间受限下的稀疏核主成分分析方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MA, WENQING: "A Weighted Kernel Principal Component Analysis and the Related Parameters Choice", ELECTRONIC TECHNOLOGY & INFORMATION SCIENCE CHINA MASTER'S THESES FULL-TEXT DATABASE, 15 September 2009 (2009-09-15), pages 15 - 35, ISSN: 1674-0246 *
YANG, SHAOHUA: "A Face Recognition Method Based On Kernel-PCA", JOURNAL OF HE BEI UNIVERCITY OF SCIENCE AND TECHNOLOGY, vol. 22, no. 03, 30 September 2008 (2008-09-30), pages 45 - 48 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111914886A (zh) * 2020-06-13 2020-11-10 宁波大学 一种基于在线简略核学习的非线性化工过程监测方法
CN111914886B (zh) * 2020-06-13 2022-07-26 宁波大学 一种基于在线简略核学习的非线性化工过程监测方法

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