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WO2018187952A1 - Procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal - Google Patents

Procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal Download PDF

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Publication number
WO2018187952A1
WO2018187952A1 PCT/CN2017/080176 CN2017080176W WO2018187952A1 WO 2018187952 A1 WO2018187952 A1 WO 2018187952A1 CN 2017080176 W CN2017080176 W CN 2017080176W WO 2018187952 A1 WO2018187952 A1 WO 2018187952A1
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Prior art keywords
training
neural network
discriminant analysis
bitmap data
sample
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PCT/CN2017/080176
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English (en)
Chinese (zh)
Inventor
邹霞
Original Assignee
邹霞
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Priority to PCT/CN2017/080176 priority Critical patent/WO2018187952A1/fr
Publication of WO2018187952A1 publication Critical patent/WO2018187952A1/fr

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

Definitions

  • the present invention relates to a kernel discriminant analysis approximation method based on a neural network, and belongs to the field of face recognition.
  • 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, and face expression analysis.
  • narrow face recognition is defined as a technology or system that enables identity confirmation, 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 has focused on identifying faces, retinas, and irises.
  • 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 features, while humans or other creatures usually do not pass fingerprints.
  • Features such as the iris distinguish individuals, so the above feature recognition does not have natural features.
  • 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 authentic 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 of the authenticated person avoiding 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, and the authenticity and reliability of the information are increased.
  • Face recognition technology based on nuclear space is one of the most widely used techniques in the field of face recognition.
  • a general nuclear subspace face recognition method flow is shown in Figure 1. Since all the training samples are used in the representation of the basis in the nuclear subspace, the projection speed of the test sample slows down as the number of training samples increases. It seriously affects the speed of face recognition, especially in the real system and the online system.
  • the present invention provides a neural network based kernel discriminant analysis approximation method, including the following steps: [0015] Step 1: Establish a training set image set, store the training set face bitmap, and read the bitmap Data
  • Step 2 performing feature extraction on the training samples in the original input space to form a training set sample set Y
  • Step three feature extraction of the training sample set Y, forming a training sample set Z
  • Step 4 training an RB F neural network by using the training set bitmap data and the feature set Z of the training set after feature extraction;
  • Step 5 Establish a test set image set, store the test set face bitmap, and read the bitmap data [0020] Step 6. Input the test set bitmap data into the trained RBF neural network to obtain the test. Set sample point
  • Step 7 Using the classifier, classify and identify the test set image.
  • step 2 above the feature extraction in the original input space is performed by the KPCA method.
  • the above step 3 performs LDA feature extraction on the training sample set Y.
  • the neural network-based kernel discriminant analysis approximation method provided by the present invention provides a neural network-based face recognition method, and an online system with a fast approximation speed and a large number of training samples. ⁇ Systems and fingerprint recognition with high recognition speed, license plate recognition and other fields will have broad application prospects.
  • FIG. 1 is a schematic flow chart of a general nuclear subspace face recognition method in the prior art
  • FIG. 2 is a schematic flow chart of a kernel discriminant analysis approximation method based on a neural network according to the present invention.
  • the present invention provides a kernel discriminant analysis approximation method based on a neural network.
  • the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
  • This embodiment mainly uses the training set image data itself and its nuclear subspace method for feature extraction. If you train an RBF neural network. This RBF neural network is used in the face recognition process, so that the test set image data can be quickly input to the result of approximating the feature extraction of the nuclear space method after inputting the RBF neural network.
  • the neural network-based kernel discriminant analysis approximation method provided by the present invention specifically includes the following steps:
  • test set bitmap data is input into the trained RBF neural network to obtain a test set sample point set.
  • the neural network-based kernel discriminant analysis approximation method provided by the present invention provides a neural network-based face recognition method, and the online system with fast approximation speed and large number of training samples is real. ⁇ Systems and fingerprint recognition with high recognition speed, license plate recognition and other fields will have broad application prospects.

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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)
  • Collating Specific Patterns (AREA)

Abstract

L'invention concerne un procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal. Le procédé consiste à : établir un ensemble image d'ensemble d'apprentissage, stocker une table de bits de visages d'ensemble d'apprentissage, et lire des données de table de bits; réaliser une extraction de caractéristique sur un échantillon d'apprentissage dans un espace d'entrée d'origine pour former un ensemble échantillon d'ensemble d'apprentissage Y; effectuer une extraction de caractéristique sur l'ensemble échantillon d'apprentissage Y pour former un ensemble échantillon d'apprentissage Z; entraîner un réseau neuronal RBF à l'aide des données de table de bits d'ensemble d'apprentissage et de l'ensemble échantillon d'ensemble d'apprentissage Z formés après l'extraction de caractéristique; établir un ensemble image d'ensemble de test, stocker une table de bits de visages d'ensemble de test, et lire des données de table de bits; entrer les données de table de bits d'ensemble de test dans le réseau neuronal RBF entraîné pour obtenir un ensemble de points d'échantillon d'ensemble de test; et classifier et reconnaître des images d'ensemble de test à l'aide d'un classifieur. Par comparaison avec l'état de la technique, le procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal, et un procédé de reconnaissance faciale basé sur un réseau neuronal ont une vitesse d'approximation rapide.
PCT/CN2017/080176 2017-04-12 2017-04-12 Procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal WO2018187952A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/080176 WO2018187952A1 (fr) 2017-04-12 2017-04-12 Procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/080176 WO2018187952A1 (fr) 2017-04-12 2017-04-12 Procédé d'approximation de noyau en analyse discriminante basé sur un réseau neuronal

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WO2018187952A1 true WO2018187952A1 (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
CN112017771A (zh) * 2020-08-31 2020-12-01 吾征智能技术(北京)有限公司 一种基于精液常规检查数据的疾病预测模型的构建方法及系统

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CN101710386A (zh) * 2009-12-25 2010-05-19 西安交通大学 一种基于相关特征和非线性映射的超分辨率人脸识别方法
CN101739555A (zh) * 2009-12-01 2010-06-16 北京中星微电子有限公司 假脸检测方法及系统、假脸模型训练方法及系统
CN102289670A (zh) * 2011-08-31 2011-12-21 长安大学 一种具有光照鲁棒性的图像特征提取方法
CN102945361A (zh) * 2012-10-17 2013-02-27 北京航空航天大学 基于特征点矢量与纹理形变能量参数的人脸表情识别方法

Patent Citations (4)

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CN101739555A (zh) * 2009-12-01 2010-06-16 北京中星微电子有限公司 假脸检测方法及系统、假脸模型训练方法及系统
CN101710386A (zh) * 2009-12-25 2010-05-19 西安交通大学 一种基于相关特征和非线性映射的超分辨率人脸识别方法
CN102289670A (zh) * 2011-08-31 2011-12-21 长安大学 一种具有光照鲁棒性的图像特征提取方法
CN102945361A (zh) * 2012-10-17 2013-02-27 北京航空航天大学 基于特征点矢量与纹理形变能量参数的人脸表情识别方法

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112017771A (zh) * 2020-08-31 2020-12-01 吾征智能技术(北京)有限公司 一种基于精液常规检查数据的疾病预测模型的构建方法及系统
CN112017771B (zh) * 2020-08-31 2024-02-27 吾征智能技术(北京)有限公司 一种基于精液常规检查数据的疾病预测模型的构建方法及系统

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