模式识别课程作业 matlab与libsvm环境 基于svm的人脸识别程序。 模式识别课程作业，利用pca与svm进行人脸识别。 matlab与libsvm环境 人脸识别

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，所用的图像处理库为 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；

matlab程序，包含了基本LBP，旋转不变LBP，均匀LBP ,旋转不变均匀LBP，CLBP等的LBP算子提取和分类，基于AR的人脸库，还有附加的一个说明文档以及自己测试的一些结果分析。

网上找的人脸识别程序，JAR文件，可以用反编译软件查看代码，学习人脸识别的同学可以参考下

Algorithm Description Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain [1]. This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” [2] For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase. This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image. Recognition accuracy is of 99.75% (only one mis-match i.e. recognition fails on image number 4 of subject 17) [1] M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision (ICCV), pages 390–393, Osaka, Japan, 1990. [2] Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242

用C#做学习型人脸识别程序（尝鲜版）DEMO和源码

《用C++语言写的完整的人脸识别程序源代码》是不错的资源哦！

模式识别课程作业，利用pca与svm进行人脸识别。 matlab与libsvm环境

SRC在人脸识别中的MATLAB程序实现，可直接运行

自己用matlab编写的基于Gabor 的2DPCA 人脸识别程序，运行速度快，识别率较高，直接输出对Yale数据库的识别率。本程序附带Yale数据库，可以直接运行。通过修改训练样本的个数，输出不同的识别律，可以研究训练样本个数对识别率的影响。