搜索资源列表
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fisher线性鉴别分析的人脸识别，在ORL库上实验，可在其他库上运行-fisher linear discriminant analysis for face recognition, in the ORL database on the test can be run on other database
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使用Fisher线性鉴别分析（FLDA）方法在ORL人脸数据库上进行人脸识别试验。ORL标准人脸库共包含40人，每人10幅共400幅BMP图像。-The use of Fisher linear discriminant analysis (FLDA) at Ways on ORL face database for face recognition test. Standard ORL face database contains a total of 40 people, 10 per pe
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提出了一种新的基于图像分块重构和线性判别分析相融合的方法，主要用于人脸识别。该方法通过计算两幅图像之间图
像块的重构均值误差，运用线性判别分析求出两幅图像降维后的欧式距离，融合重构误差和欧式距离计算这两幅图像之间的差别
程度。-A new block-based image reconstruction and the integration of linear discriminant analysis method is mainly used for face recognitio
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Paper on non linear discriminant analysis.
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基于matlab的人脸识别程序，可以实现简单的人脸识别功能，算法基于LDA（linear discriminant analysis）-Matlab-based face recognition program, you can achieve a simple face recognition algorithm based on LDA (linear discriminant analysis)
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基于Probabilistic Linear Discriminant Analysis的人脸识别matlab源码-matlab code for probabilistic linear discriminant analysis applied to face recognition
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This Linear Discriminant Analysis code. It is made for image processing, especially in face recognition.-This is Linear Discriminant Analysis code. It is made for image processing, especially in face recognition.
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fisher linear discriminant analysis
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线性判别分析的人脸识别系统代码matlab程序，直接运行gui界面文件就可以了-Linear discriminant analysis for face recognition system code matlab procedures, directly run gui interface files can be
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线性判别分析的人脸识别系统代码 实现二维人脸的识别-Linear discriminant analysis for face recognition system code
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Face Recognition System based on Fisher Linear Discriminant Analysis (Fisher LDA)
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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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在MATLAB2014b版本，进行人脸识别，包含PCA降维、LDA线性判别、KNN最近邻算法等技术(In MATLAB2014b version, face recognition is carried out, including PCA dimension reduction, LDA linear discriminant analysis, KNN nearest neighbor algorithm and so on.)
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