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kernel-ica1_1.tar
- 可以用于人脸识别的核ICA算法，在ICA基础上改造过来的。-can be used for nuclear ICA face recognition algorithm based on the ICA transformation overnight.
kpca.rar
- kpca 用于人脸识别等的matlab源码,KPCA for face recognition matlab source, etc.
Face_Recognition.zip
- Face Recognition, Face Detection, Lausanne Protocol, 3D Face Reconstruction, Principal Component Analysis, Fisher Linear Discriminant Analysis, Locality Preserving Projections, Kernel Fisher Discriminant Analysis,Face Recognition, Face Detection, L
algorithm-of-face-recognition
- 主要介绍了各种关于人脸识别的核心算法，如LGBP，AdaBoost，SV的Kernel判别及基于特定人脸子空间-Introduces a variety of core face recognition algorithms, such as LGBP, AdaBoost, SV and the Kernel discriminant subspace based on a specific face
ar_dct_kda
- 在AR人脸库上进行DCT变换，使用DCT变换后的图像进行 kernel fisher discriminant analysis,其中kernel 函数可以自己选择-In the AR face database on the DCT transform, using the DCT transformed image kernel fisher discriminant analysis, which can choose the kernel function
KernelTracking
- A new approach toward target representation and localization, the central component in visual tracking of non-rigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kern
pcajiafisher
- pca+fisher是将核函数应用到人脸识别研究中去-pca+ fisher is the kernel function is applied to face recognition research go
F_KDDA_PolyPro
- kernel methods for face recognition
KPCAandSVM
- KPCA与SVM共同用于人脸识别 SVM提高了分类效果 KPCA是一种借鉴SVM中核函数的一种较好的特征提取方法-KPCA and SVM for face recognition SVM together to improve the classification results from KPCA is a kernel function in SVM a better feature extraction method
KernelBasedObjectTracking
- A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel.
kernel-ica1_1
- 基于核函数的主分量分析法源代码，可用于人脸识别-Kernel-based principal component analysis source code, can be used for face recognition
AR_SKFA
- 在ar人脸库上的sparse kernel feature analysis程序，该程序挨个计算kernel空间鉴别向量，最后组成鉴别向量集进行分类-In the ar face database on the sparse kernel feature analysis program, which identified one by one calculation kernel space vector, the final set of vectors to classify the co
jiyutezhengronghehemohuhepanbian
- 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定 位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基 准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进 行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最 近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和
3DFaceRecognitionBasedon3DLBPandKernelDiscriminant
- 二维照片的人脸识别对光照、姿态和化妆等因素很敏感，故提出了一种将三维局部二值模式(3DLBP)和核享，1剐分析(KDA)相结合的三维人脸识剐方法．采用3DLBP描述人脸深度图像的特征，高斯核函数KDA 作为分类器，使用Chi平方统计改进高斯核函数、采用FRGC v2．0中2003春季采集的三维人脸库进行实验．实验结果表明，该 方法在每人2个训练样本时，识别率为91．8％，而PCA和3DLBP的识别率分别为60．4％和78．3％；当每人的训练样本数增至6个时，识别率为98．4％，而PCA和3D
KPCA
- 在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验. -Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and reco
Linux_kernel_debug
- 调试是软件开发过程中一个必不可少的环节，在 Linux 内核开发的过程中也不可避免地会面对如何调试内核的问题。-Debugging is the process of software development is an indispensable link in the Linux kernel development process will inevitably face the issue of how to debug the kernel.
facerecgnize
- 模式识别课程作业，pca和kpca，以及一个人脸可。其中kpca的核函数是多项式。-Pattern Recognition course assignments, pca and kpca, and a person can face. Where the kernel function is polynomial kpca.
face-recog-using-kernel-pca
- face recognition using PCA
Face-recognition
- 本文针对人脸图像的特点，选取一组Gabor 小波核，并用这组Gabor 小波核对人脸图像进行Gabor 小波变换，提取人脸 图像的有效信息。在此基础上，用2DPCA 对Gabor 小波提取的 数据矩阵进行降维，最后用最近邻法对人脸进行分类。-In this paper, the characteristics of face images, select a set of Gabor wavelet kernel, and check with this set of Gabor wav
Kernel PCA and Pre-Image Reconstruction
- Kernel Pca for face recogntion