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本程序实现了用pca,svm实现人脸表情的识别,很精确的结果,具有很高的识别效果-This application implements with pca, SVM realize the recognition face expression, very accurate results, has the very high recognition result
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本程序实现了用pca以及kpca,svm实现人脸表情的识别,很精确的结果,具有很高的识别效果-This application implements with pca and kpca, SVM realize the recognition face expression, very accurate results, has the very high recognition result
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Face recognition with SVM
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本程序使用MATLAAB R2014a 编写,基于PCA_SVM的人脸识别程序。程序包括主成份分析、SVM核函数,并附带了人脸库,使之能够直接调用人脸库图像进行人脸识别-The program uses MATLAAB R2014a written procedure based on recognition of PCA_SVM. Program includes principal component analysis, SVM kernel function, and comes face
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Face recognition written in MATLAB. This code implements Fisher s Discriminant Analysis and SVMs using LIBSVM.-The motivation of this project is to implement several techniques for face recognition:
Principal Component Analysis
Fisher’s Linear
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基于SVM和PCA的人脸识别,使用了ORL人脸数据集和libsvm.jar-Face recognition based on SVM and PCA. ORL faces dataset and libsvm.jar are used
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基于PCA-SVM的人脸识别,平均识别率达83 ,是基于matlab开发的。-PCA-SVM-based face recognition, the average recognition rate of 83 , based on matlab development.
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人脸识别中的光照处理方法,实现了对10个数字音的识别,包括最小二乘法、SVM、神经网络、1_k近邻法。- Face Recognition light treatment method, To achieve the recognition of 10 digital sound, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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人脸识别 FA和BP网络的预测 LVQ神经网络的预测——人脸识别 SVM分类与回归 Elman神经网络预测-Face recognition, FA and BP network prediction, LVQ neural network prediction- face recognition, SVM classification and regression, Elman neural network prediction
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用于图像处理的独立分量分析,人脸识别中的光照处理方法,包括最小二乘法、SVM、神经网络、1_k近邻法。- Independent component analysis for image processing, Face Recognition light treatment method, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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对jaffe人脸库进行识别测试的主程序,将jaffe人脸库分为训练集和测试集两部分,首先对图片进行LBP+LPQ特征提取,然后svm分类识别,统计识别率-Jaffe face for the identification of the main test will jaffe face is divided into a training set and a test set of two parts, the first of LBP+LPQ image feature extractio
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阐述了负荷预测的应用研究,Gabor小波变换与PCA的人脸识别代码,包括最小二乘法、SVM、神经网络、1_k近邻法。- It describes the application of load forecasting, Gabor wavelet transform and PCA face recognition code, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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人脸识别(PCA+SVM)
文件中包含训练样本,运行后,能进行人脸识别,采用PCA进行降维,利用SVM 进行分类识别-Face recognition(PCA+SVM)
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基于PCA加上SVM的人脸识别算法 使用libsvm作为SVM工具箱-Face recognition based on PCA and SVM
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使用了两种方法实现人脸识别:hog+svm,KNN(Two methods are used to implement face recognition: hog+svm, KNN)
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编写了用户界面程序实现ocr人脸数据集的识别,使用了svm分类器(A user interface program is developed to realize the recognition of OCR face data set, and the SVM classifier is used)
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本程序中,利用了LBP特征对人脸特征进行提取,并且利用SVM对提取的人脸特征进行训练和识别,其中,所用的图像处理库OpenCV2.4.9版本;通过对人脸库中的标准标本进行测试,算法识别率高达95%以上;(LBP features extract facial features, and use SVM to extract and recognize the facial features. The OpenCV2.4.9 version of the image processing libr
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运用LVQ进行人脸识别,得到的误差结果较好,同时又BP,SVM与其进行比较(The use of LVQ for face recognition is better, and the comparison between BP and SVM is compared.)
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完整人脸识别程序和说明,该程序是中的人脸检测系统的克隆。
而神经网络,它是基于支持向量机(SVM)Machin(Complete face recognition program and descr iption)
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将理论知识、科学研究和工程实践有机结合起来,内容涉及数字图像处理和识别技术的方方面面,包括图像的点运算、几何变换、空域和频域滤波、小波变换、图像复原、形态学处理、图像分割以及图像特征提取的相关内容;同时对于机器视觉进行了前导性的探究,重点介绍了两种目前在工程技术领域非常流行的分类技术——人工神经网络(ANN)和支持向量机(SVM),并在人脸识别这样的热点问题中结束《精通Matlab数字图像处理与识别》。(Combining theoretical knowledge, scientific re
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