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Description: VC编写的基于PCA和LBP的人脸识别程序-VC prepared by the PCA and the LBP-based face recognition program
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Size: 7403520 |
Author: 林丹 |
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Description: 人脸识别中有用的文件包，在PCA，和LBP中会经常用到-Package useful in face recognition, in the PCA, and often used in LBP
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Size: 1024 |
Author: 李杏杏 |
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Description: 图像融合算法，关于PCA、小波、拉普拉斯等-image fusion
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Size: 31744 |
Author: zhuyahui |
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Description: 基于LBP的人脸检测，其中PCA用来降维，LBP用于提取纹理特征-PCA used for dimensional reduction, LBP used to extract the texture characteristics
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Size: 15812608 |
Author: jianghui |
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Description:
Dear all find here important tool box for dimension reduction. TOOL box contaion PCA, ICA, LBP, Kernel LDA, Kernel PCA etc
Enjoy-
Dear all find here important tool box for dimension reduction. TOOL box contaion PCA, ICA, LBP, Kernel LDA, Kernel PCA etc
Enjoy!!!!!!!!!!!!!!!!
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Size: 1049600 |
Author: xiaomingw |
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Description: Gabor小波人脸识别，LBP特征提取，PCA，LPP降维。自己做的毕业设计。-Gabor wavelet face recognition, LBP feature extraction, PCA, LPP dimensionality reduction. Own graduation design.
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Size: 20480 |
Author: 张彬 |
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Description: FaceRecognizeCode人脸识别的源代码。基于LBP——pca
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Size: 197143 |
Author: longzaitianya1990 |
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Description: 为有效解决局部二元模式(LBP)在人脸识别特征提取时维数过高的问题，提出了一种结合LBP特征和主成分分析(PCA)的人脸识别方法．-To effectively solve the local binary pattern (LBP) feature extraction in face recognition problem of high dimensionality, we propose a combination of LBP features and principal component analysis (PCA) face recognition method.
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Size: 326656 |
Author: 雷晨雨 |
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Description: The ear, as a biometric, has been given less
attention, compared to other biometrics such as fingerprint,
face and iris. Since it is a relatively new biometric, no
commercial applications involving ear recognition are
available. Intensive research in this field is thus required to
determine the feasibility of this biometric. In medical field,
especially in case of accidents and death, where face of patients
cannot be recognized, the use of ear can be helpful. In this
work, yet another method of recognizing people through their
ears is presented. Local Binary Patterns (LBP) is used as
features and the results are compared with that of Principal
Components Analysis (PCA). LBPhas a high discriminative
power, tolerance against globalillumination changes and low
computational load. Experiments were done on the Indian
Institute of Technology (IIT) Delhi ear image database and
results show that LBP yields a recognition rate of 93 while
PCA gives only 85 .-The ear, as a biometric, has been given less
attention, compared to other biometrics such as fingerprint,
face and iris. Since it is a relatively new biometric, no
commercial applications involving ear recognition are
available. Intensive research in this field is thus required to
determine the feasibility of this biometric. In medical field,
especially in case of accidents and death, where face of patients
cannot be recognized, the use of ear can be helpful. In this
work, yet another method of recognizing people through their
ears is presented. Local Binary Patterns (LBP) is used as
features and the results are compared with that of Principal
Components Analysis (PCA). LBPhas a high discriminative
power, tolerance against globalillumination changes and low
computational load. Experiments were done on the Indian
Institute of Technology (IIT) Delhi ear image database and
results show that LBP yields a recognition rate of 93 while
PCA gives only 85 .
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Size: 247808 |
Author: krish |
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Description: 使用LBP和主成分分析法，进行人脸识别的程序，可以获得不错的效果。-The use of LBP and principal component analysis (pca) for face recognition program, can obtain good effect.
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Size: 6302720 |
Author: |
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Description: 人脸识别系统采用了LBP PCA 2CKPCA 2BSVM算法实现的，效率高。-Face Recognition System using LBP PCA 2B KPCA 2B SVM algorithm, and high efficiency.
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Size: 20480 |
Author: marry |
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Description: 代码分为两部分，训练和测试，1、将训练图像的地址保存在taining_set.txt文件中，2、分别对训练图像进行预处理和特征提取，提取图像的LBP纹理图，再使用PCA方法进行降维。3、使用cvSVN进行分类-Face recognition
The code is divided into two parts, training and testing, 1, the training image is stored in the taining_set.txt file, 2, respectively, the training image preprocessing and feature extraction, extract the image LBP texture map, and then use the PCA method Dimensionality reduction. 3, the use of cvSVN classification
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Size: 1965056 |
Author: 李明 |
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