搜索资源列表
PCAPLDA
- PCA+LDA人脸识别，PCA降维到N-C,（N为训练样本数，C为类别数）使得Sw非奇异，主要是解决小样本，数据集为ORL，每类取9（可改）个图片-PCA+LDA recognition, PCA dimensionality reduction to NC, (N is the number of training samples, C is the number of categories) make Sw nonsingular, mainly to resolve the small s
lle
- lle（局部线性嵌入）算法的源代码，适合进行人脸识别等，可降维。-lle (LLE) algorithm source code, suitable for face recognition, can reduce the dimension.
BDLPP
- 该源代码是人脸识别中的二维降维方法BDLPP的源代码，下载下来就可执行，其代码简单易理解。-The source code is a two-dimensional face recognition dimensionality reduction method BDLPP source code can be downloaded from execution, the code is simple and easy to understand.
arRecByEulidDist
- 利用欧氏距离测试ar人脸数据库，该处的数据库经过了PCA降维处理； 训练样本为共计100个人的700幅图像降维数据 仅供参考，和交流之用。 - by using Euclidean distance test of AR face database, the database through the PCA dimension reduction training sample reduction data for 700 images of 100 individua
PCA-LDA-FaceRecognization
- 基于PCA-LDA的人脸识别算法。用PCA降维，LDA进行分类-PCA-LDA FaceRecognization
pca_face_rec
- 用pca降维数据进行人脸识别，得到一定的主成份数据，而后根据与主成份数据之间的距离来判断是否属于同一个人的照片-use the pca algorithm to Face recognition
main
- 本程序实现人脸对齐功能，它通过提取人脸特征，利用PCA降维，实现人脸对齐效果-This program implements the face alignment feature, it is through facial feature extraction using PCA dimensionality reduction to achieve the effect of face-aligned
renlianshibie
- 利用PCA进行降维，并利用adaboost进行分类的人脸识别方法描述-Use PCA dimensionality reduction , face recognition method using adaboost classification descr iption
LGEandKGEandOLGEandKPCAandKLPP
- 一系列降维算法，包括LGE,KGE,KLPP,OLEG,KPCA，将高维空间的数据降低的低维，主要用来进行人脸识别以等方面的特征提取,总共5个算法。-isometric projection to feature extraction
PCA_based-Face-Recognition-System
- matlab人脸识别代码，用了PCA降维处理，可以进行简单的人脸识别-Face recognition matlab code, using the PCA dimension reduction, face recognition can be a simple
PCA
- PCA人脸识别降维，可重构图像观察降维效果-Face recognition PCA dimension reduction, can test the effect of dimensionality reduction
face
- 先用PCA降维，然后使用OMP识别人脸图像。人脸库是ORL人脸库。-First with PCA dimensionality reduction, and then use the OMP face image recognition. Face is the ORL.
PCA--algorithm
- 人脸特征抽取。主成分分析法。特征降维。样本矩阵。-Facial feature extraction。Principal component analysis (pca).Feature dimension reduction.The sample matrix
pca20111111
- 实现对人脸数据库的读取，并对其进行降维，可以降维到小于人脸数据样本一下的维数-Dimension achieve human face reads, and its dimensionality reduction, can be reduced to less than one-dimensional data samples at the face
fastPCA
- PCA降维用于实现人脸识别和主成分分析，输入时n维输出是k维-PCA dimensionality reduction for face recognition and principal component analysis, n-dimensional output when the input is k-dimensional
Yale_face-recognition_pca
- 使用yale人脸数据库，用PCA降维方法来对人脸数据进行分类的MATLAB代码，附有人脸图片-Yale face , using PCA dimension reduction method to classify data of face of MATLAB code, and comes with a human face images
pca
- 人脸搜索简单实现，使用主成分分析算法(PCA)，依赖opencv，对若干张人脸图片进行PCA降维处理，然后将输入人脸图片与降维后的数据做比较，根据权重输出结果，权重越大则人脸越相似-Simple face search, using the principal component analysis (PCA) algorithm, opencv, for a number of face images for PCA dimension reduction, and then the input
PCAPLPP
- 在人脸识别当中，针对LPP是一个局部化的线性降维处理，我们先提出来先对其进行预处理，然后再利用LPP。-In face recognition, in view of the LPP is a localization of linear dimension reduction processing, we first put forward the preprocessing, and then re-use LPP
KPCA-ORL
- 随着人脸识别越来越受到重视，而人脸识别中特征降维又很重要，所以特征降维算法也很重要。因此PCA降维算法很重要-Dimensionality reduction for Face Recognition
svm_matlab_facerecognition
- 利用PCA算法进行特征提取和数据降维，再用SVM算法进行人脸识别的程序，里面有人脸数据库！-Use PCA algorithm for feature extraction and data reduction, and then SVM algorithm recognition program, which was face !