搜索资源列表
pca
- 是用主元分析法实现的较高识别率的人脸识别，开发环境是MATLAB-It is the principal component analysis method is used to implement the high recognition rate of face recognition, development is the MATLAB environment
HLBP
- 基于Haar特征的LBP算法，在人脸识别方面会更加迅速，而且识别率很高-LBP algorithm based on Haar features in face recognition will be more rapid, and high recognition rate
face
- 人脸识别例程，识别率达到95 左右，有人脸库，下载可直接运行-Recognition routines, the recognition rate of around 95 percent, it was the face , download can be run directly
KL_SVD_face_recognition
- PCA主成分分析，采用KL投影和SVD分解提取人脸特征向量，最后采用最近邻判别法计算识别率。-Face recognition based on PCA. KL projection and SVD are used to extract face eigenvectors. Recognition rate is calculated by k nearest neighbors(KNN) method.
Face-recognition
- 基于BP神经网络的人脸识别MATLAB代码，构造、训练BP神经网络并计算其识别率。-Face recognition based on BP neural network MATLAB code, structure, training BP neural network and computing its recognition rate.
llr
- 局部线性回归(llr,local linear regression)将具有偏转角度的人脸进行姿态校正。从而提高人脸识别率。-Local linear regression (llr, local linear regression) will face people with a deflection angle of the posture correction. Thereby enhancing the recognition rate.
pca
- 一个修改后的PCA进行人脸识别的Matlab代码，识别率达到88％-A revised PCA face recognition Matlab code recognition rate of 88
KPCAface_rec
- KPCA算法人脸识别，识别率可达到90 以上，附带人脸库-KPCA algorithm recognition, recognition rate can reach more than 90 percent, with face
chap15
- 人脸识别的核心算法实现,达到很高的识别率-the core of face recognition algorithm，to chieve a high recognition rate.
PCA
- 基于pca方法用于人脸识别，并在YALE和ORL数据库上验证其识别率-Used for face recognition based on pca method, and verify its recognition rate on ORL and YALE
2dpca
- 对人脸进行2DPCA提取特征并降维,提高人脸识别率-On people s faces 2D PCA feature extraction and dimensionality reduction, improve face recognition rate
Fisher
- 运用Fisher准则进行人脸识别，包含创建人脸数据库，识别程序，识别率高-Fisher criterion using face recognition, face consists of creating a , identification procedures, recognition rate
Eigenface
- 人脸识别Eigenface算法的完整实现，主要基于PCA(主成成分分析)和kNN(k近邻)分类器实现，测试模板库基于ORL和yale，可以达到98 的识别率。-Eigenface complete recognition algorithm, mainly based on PCA (Principal Component Analysis into) and kNN (k nearest neighbor) classifier implementation, test template li
FisherFace
- 基于LDA线性辨别分析的人脸识别算法，采用KNN分类器，可直接运行，自带数据库，识别率有88 。-LDA face recognition algorithm based on linear discriminant analysis, using KNN classifier, can be directly run, comes with a , the recognition rate of 88 .
biaoqingshibie
- 是对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 -Is jaffe face recognition test the main library, the library will jaffe face divided into training and test sets of two parts, the first of LBP+LPQ image feature extrac
pcalda
- 基于pca和lca的人脸识别程序, 人脸库分为训练集和测试集两部分，统计识别率 -Based on pca face recognition program and lca, the face is divided into a training set and a test set of two parts, the recognition rate statistics
face-recognition
- 人脸识别软件仅仅单独采用一种现有的人脸识别方法一般都不会取得很好的识别效果。各种技术和方法都有自己不同的适应环境和各自的特点。如果我们想进一步提高人脸识别系统的识别率，可以考虑使用数据融合理论，将不同的方法综合起来，相互补充，来取得很好的人脸识别效果。这也是为人脸识别的研究趋势之一。-Only uses a single conventional face recognition methods are generally not made good recognition. Various t
pca_svm
- PCA+svm算法进行人脸识别，识别率在百分之80~90- Face recognition algorithm Pca+ support vector machine Recognition rate of about ninety percent, interested friends can be used as a reference
fd
- 人脸识别，识别率较高，基于matlab编程-FACE RECGONTION
goufui
- 具有丰富的参数选项,Gabor小波变换与PCA的人脸识别代码，通过反复训练模板能有较高的识别率。- It has a wealth of parameter options, Gabor wavelet transform and PCA face recognition code, Through repeated training vPAIMMQlate have higher recognition rate.