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Description: 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.
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Author: redapple |
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Description: 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.
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Author: redapple |
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Description: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms
and Matlab source codes for the kernel direct discriminant analysis (KDDA) -Face Recognition Using Kernel Direct Disc riminant Analysis Algorithms and Matlab sourc e codes for the kernel direct discriminant anal ysis (KDDA)
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Author: cy |
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Description: 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 kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking .
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Author: |
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Description: 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
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Size: 11264 |
Author: zhangwenming |
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Description: kernel methods for face recognition
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Size: 2048 |
Author: mahdi |
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Description: 改进的核函数算法及其在人脸识别中的应用研究，很好的东东！-Kernel function to improve the algorithm and its application in face recognition research
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Size: 3072 |
Author: joyboble |
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Description: 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
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Size: 224256 |
Author: 付赛男 |
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Description: 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. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.
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Size: 2459648 |
Author: Ali |
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Description: 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
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Size: 27648 |
Author: xiechaocheng |
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Description: Face Recognition, Face Detection, Lausanne Protocol, 3D Face Reconstruction,
Principal Component Analysis, Fisher Linear Discriminant Analysis,
Locality Preserving Projections, Kernel Fisher Discriminant Analysis
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Size: 3445760 |
Author: psomkuan |
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Description: 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
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Size: 2048 |
Author: 邵珠立 |
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Description: 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 composition of differential
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Author: 邵珠立 |
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Description: 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Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method.
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Size: 375808 |
Author: MJ |
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Description: 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3DLBP的识别率分别为87．8％和96．3-3D Face Recognition Based on 3DLBP and Kernel Discriminant Analys
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Size: 247808 |
Author: 宋祖波 |
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Description: 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 recognition test.
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Size: 1024 |
Author: 李海 |
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Description: 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.
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Size: 933888 |
Author: 庄德安 |
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Description: 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.
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Size: 3430400 |
Author: perfy yang |
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Description: face recognition using PCA
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Size: 84992 |
Author: Waqar |
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Description: 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 wavelet Gabor face image wavelet transform to extract useful information of face images. On this basis, with 2DPCA on data extracted Gabor wavelet dimension reduction matrix, and finally with the nearest neighbor method to classify the human face.
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Size: 55296 |
Author: 李 |
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