Description: 基于稀疏表示的人脸识别，很经典的人脸识别文章-Expressed based on the sparse face recognition, face recognition is a classic article Platform: |
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Author:钱明 |
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Description: NMFs算法（带稀疏度约束的非负稀疏矩阵分解）用于实现基于人脸局部特征的人脸识别，通过近似的矩阵分解进行空间降维。-NMFS algorithm (sparse with degree-constrained non-negative sparse matrix factorization) for the realization of human faces based on local features of face recognition, through approximate matrix factorization space dimensionality reduction. Platform: |
Size: 23336960 |
Author:heying |
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Description: Matching Pursuit方法，经典的稀疏表示方法，可以用人脸识别和图像分类，图像去噪，现在非常流行。-Matching Pursuit method, sparse representation of the classic, you can use face recognition and image classification, image denoising, now very popular. Platform: |
Size: 1880064 |
Author:高尚兵 |
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Description: 该源码实现了使用基于稀疏表示的人脸识别算法。使用GPSR作为l1模最小化方法。-This pack of code implement a imges-based face recognition using sparse representation classification. In the algorithm, i employ GPSR as tool to complete the optimization procedure of l1-minimization. Platform: |
Size: 8192 |
Author:zhang chao |
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Description: 判别稀疏非负矩阵分解，提出这个新算法，来进行人脸识别，比传统的NMF和一些其他的扩展算法效果好-Sparse non-negative matrix factorization judge proposed the new algorithm for face recognition, than the traditional extension of NMF algorithm and some other good results Platform: |
Size: 220160 |
Author:zhangwei |
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Description: 此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列
为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do？的ObjectID = 13701＆的objectType =FILE下载。
-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
split up Sparse Network of Winnows is presented to speed up the
original classifier. Finally, the features and classifier are combined
for the task of frontal face detection. Detection results are presented
for the MIT+CMU and the BioID databases. With regard to this
face detector, the Receiver Operation Characteristics curve for the
BioID database yields the best published result. The result for the
CMU+MIT database is comparable to state-of-the-art face detectors.
A Matlab version of the face detection algorithm can be downloaded
from http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do?objectId=13701&objectType=FILE. Platform: |
Size: 1397760 |
Author:霞 |
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Description: 基于稀疏表示的人脸识别，里面有9种求1范数的方法-Face recognition based on sparse representation, there are nine kinds of seeking a method of norm Platform: |
Size: 78848 |
Author:wangqiang |
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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: |
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Author:刘大明 |
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Description: 主要用于解决模式识别中稀疏表示人脸识别核心问题L1范数源代码，程序采用同伦算法设计的，在目前稀疏表示多种算法中，同伦算法是性能公认最好的.-Mainly used to solve the sparse representation of face recognition pattern recognition in the core of L1 norm source code, the program designed using the homotopy algorithm, sparse representation in a variety of current algorithms, the homotopy algorithm is recognized as the best performance. Platform: |
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Author: |
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Description: 发表于ECCV上的一篇用于人脸识别的算法，Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary -At ECCV on an algorithm for face recognition, Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary Platform: |
Size: 13312 |
Author:joe |
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Description: 文獻「Robust Sparse Coding for Face Recognition」
及matlab代碼-「Robust Sparse Coding for Face Recognition」 and matlab source code. Platform: |
Size: 1144832 |
Author:游炳賢 |
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Description: 用matlab实现的基于稀疏表示的人脸识别方法。其中解稀疏表示时，包含了各种方法。-Using matlab face recognition method based on sparse representation. Solution of the sparse representation, contains a variety of methods. Platform: |
Size: 47104 |
Author:wangbinbin |
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Description: 基于稀疏理论的人脸识别，比较经典的中文文章，应该对大家有用-Face recognition based on sparse theory, more classic Chinese article, it should be useful to everyone Platform: |
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Author:chris |
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Description: In this paper, we propose a two-phase test sample
representation method for face recognition. The first phase of
the proposed method seeks to represent the test sample as
a linear combination of all the training samples and exploits
the representation ability of each training sample to determine
M “nearest neighbors” for the test sample. The second phase
represents the test sample as a linear combination of the
determined M nearest neighbors and uses the representation
result to perform classification. We propose this method with the
following assumption: the test sample and its some neighbors
are probably from the same class. Thus, we use the first phase
to detect the training samples that are far from the test sample
and assume that these samples have no effects on the ultimate
classification decision. This is helpful to accurately classify the
test sample. We will also show the probability explanation of
the proposed method. A number of face recognition experiments
show that our method performs very well. Platform: |
Size: 460458 |
Author:may@uestc.edu.cn |
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Search - sparse face Recognition