Location:
Search - laplacian face
Search list
Description: 一种很重要的非监督降维方法，是流形学习算法Laplacian Eigenmap 的线性化方法，在人脸识别中效果非常好。-A very important method of unsupervised dimensionality reduction, manifold learning algorithm is Laplacian Eigenmap linearization method is very effective in face recognition.
Platform: |
Size: 1024 |
Author: 仲国强 |
Hits:
Description: face recognition system in matlab-face recognition system in matlab...
Platform: |
Size: 4487168 |
Author: vicky |
Hits:
Description: 本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of
faces can be effectively modeled by low dimensional
linear spaces, we propose a novel linear subspace
learning method for face analysis in the framework of
graph embedding model, called Semi-supervised
Graph Embedding (SGE). This algorithm builds an
adjacency graph which can best respect the geometry
structure inferred from the must-link pairwise
constraints, which specify a pair of instances belong to
the same class. The projections are obtained by
preserving such a graph structure. Using the notion of
graph Laplacian, SGE has a closed solution of an
eigen-problem of some specific Laplacian matrix and
therefore it is quite efficient. Experimental results on
Yale standard face database demonstrate the
effectiveness of our proposed algorithm.
Platform: |
Size: 2048 |
Author: 刘国胜 |
Hits:
Description: Laplacian Eigenmap.This contains matlab programs to calculate Laplacian Eigenmap.this is applied for face images.
Platform: |
Size: 779264 |
Author: pulak |
Hits:
Description: laplacian 人脸识别，对研究人脸识别、特征提取，LPP 很有用。-It is a useful paper about laplacian face recognition, which is also useful for feature extraction and lpp.
Platform: |
Size: 2226176 |
Author: xiaoxiao |
Hits:
Description: 强壮的人脸识别系统，发表于cvpr2011年，程序是应用matlab实现-Recently the sparse representation (or coding) based classifi cation (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as
a sparse linear combination of the training samples, and
the representation fi delity is measured by the � 2-norm or
� 1-norm of coding residual. Such a sparse coding model
actually assumes that the coding residual follows Gaus-
sian or Laplacian distribution, which may not be accurate
enough to describe the coding errors in practice. In this
paper, we propose a new scheme, namely the robust sparse
coding (RSC), by modeling the sparse coding as a sparsity-
constrained robust regression problem. The RSC seeks for
the MLE (maximum likelihood estimation) solution of the
sparse coding problem, and it is much more robust to out-
liers (e.g., occlusions, corruptions, etc.) than SRC. An
effi cient iteratively reweighted sparse coding algorithm is
proposed to solve the RSC model. Extensive
Platform: |
Size: 1216512 |
Author: 刘大明 |
Hits:
Description: face recognition using laplacian face approach
Platform: |
Size: 2048 |
Author: digs |
Hits:
Description: 人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the 𝑙 2-norm or 𝑙 1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsityconstrained
robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the
sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is
proposed to solve the RSC model.
Platform: |
Size: 18704384 |
Author: 徐波 |
Hits:
Description: 最新最强MATLAB降维工具箱，可用于人脸识别，模式识别，机器学习，数据挖掘，图像处理等领域，里面包含的算法有PCA,LDA,KPCA,KLDA,Laplacian，LPP,MDS,NPE,SPE,LLC,CFA,MCML,LM-The latest and greatest dimension reduction MATLAB toolbox can be used for face recognition, pattern recognition, machine learning, data mining, and other areas of image processing, which contains the algorithm PCA, LDA, KPCA, KLDA, Laplacian, LPP, MDS, NPE, SPE, LLC, CFA, MCML, LMNN etc.
Platform: |
Size: 1042432 |
Author: FDX |
Hits:
Description: Laplacian smoothing transform (lst) for face recognition in matlab
Platform: |
Size: 3828736 |
Author: Nono Heryana |
Hits: