Description: Sparse Representation or Collaborative Representation: Which Helps Face Recognition? This code devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification. Platform: |
Size: 3300612 |
Author:674946694@qq.com |
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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: Locally Adaptive Sparse Representation for Detection, Classification, and Recognition. Lectuures given by Prof Trac Tran from john Hopkins university Platform: |
Size: 2105344 |
Author:huutan86 |
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Description: 求解l1范式的值，用于压缩感知中的稀疏表示。进行分类-Solving the value of l1 paradigm for compressed sensing of sparse representation. Classification Platform: |
Size: 3072 |
Author:zl |
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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 |
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Description: Sparse Representation for accurate classification of corrupted and occluded facial expressions使用稀疏表示方法对有遮挡和腐蚀的人脸表情图像进行分类-Sparse Representation for accurate classification of corrupted and occluded facial expressions Platform: |
Size: 129024 |
Author:sun |
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Description: 运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。-Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection. Platform: |
Size: 11820032 |
Author:高晨旭 |
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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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Description: 一个自己编的稀疏表示分类程序（SRC），以帮助了解SRC的原理和算法。-A self sparse representation classification (SRC) program, to help understand the principles and algorithms of the SRC. Platform: |
Size: 9632768 |
Author:韩超 |
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Description: 稀疏表示分类算法在ORL人脸库上的实验，参考文章：
Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31(2): 210-227.
-Sparse representation classification algorithms on ORL face experiments, refer to the article:
Wright J, Yang AY, Ganesh A, et al Robust face recognition via sparse representation [J] Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31 (2):.. 210-227. Platform: |
Size: 3481600 |
Author:xiaoxiao |
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Description: This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database-This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database Platform: |
Size: 1664000 |
Author:Jashpreet |
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Description: Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy
advantage of sparse representation classification (SRC) in the area of image classification. Those two
methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to
noise while SRC is known to be time-consuming. Platform: |
Size: 7000064 |
Author:mmaawadi
|
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