半随机子空间的LDA人脸识别方法

小样本问题和对局部变化（如遮挡、表情、光照等）识别的不鲁棒性是线性判别分析（LDA）在处理人脸图像时所常面临的问题。针对LDA的这些不足,提出了一种基于LDA的半随机子空间方法（SemiRS-LDA）。与传统的基于整个人脸样本特征集采样的随机子空间方法不同的是,SemiRS-LDA将随机采样建立在人脸图像的子图像上。该方法首先将人脸图像集划分成若干个子图像集,然后将随机子空间方法应用于每个子图像集上并构建多个LDA分类器,最后使用投票方法将各分类器进行组合。在两个标准人脸数据库（AR、ORL）上进行了实验,结果表明了所提方法不仅能获得较高的识别性能,而且对图像的光线、遮挡等也具有较强的鲁棒性。     

特征采样和特征融合的子图像人脸识别方法

提出一种基于特征采样和特征融合的子图像人脸识别方法(RS-SpCCA).首先,对子图像进行特征采样;然后,将全局特征和采样后的特征使用CCA进行信息融合,以获取包含全局特征和局部特征的相关特征;最后,在相关特征上构建分量分类器.在该方法中,特征采样是为了构建更多且多样的分量分类器;而引入特征融合思想是为了充分利用图像的全局特征.AR,Yale和ORL这3个数据库上的实验结果表明,基于特征采样和特征融合的子图像方法(RS-SpCCA)优于单纯的信息融合方法(SpCCA)和特征采样方法(Semi-RS).     

Face recognition using second-order discriminant tensor subspace analysis

Discriminant information (DI) plays a critical role in face recognition. In this paper, we proposed a second-order discriminant tensor subspace analysis (DTSA) algorithm to extract discriminant features from the intrinsic manifold structure of the tensor data. DTSA combines the advantages of previous methods with DI, the tensor methods preserving the spatial structure information of the original image matrices, and the manifold methods preserving the local structure of the samples distribution. DTSA defines two similarity matrices, namely within-class similarity matrix and between-class similarity matrix. The within-class similarity matrix is determined by the distances of point pairs in the same class, while the between-class similarity matrix is determined by the distances between the means of each pair of classes. Using these two matrices, the proposed method preserves the local structure of the samples to fit the manifold structure of facial images in high dimensional space better than other methods. Moreover, compared to the 2D methods, the tensor based method employs two-sided transformations rather than single-sided one, and yields higher compression ratio. As a tensor method, DTSA uses an iterative procedure to calculate the optimal solution of two transformation matrices. In this paper, we analyzed DTSA's connections to 2D-DLPP and TSA, theoretically. The experiments on the ORL, Yale and YaleB facial databases show the effectiveness of the proposed method.     

Evolved feature weighting for random subspace classifier.

The problem addressed in this letter concerns the multiclassifier generation by a random subspace method (RSM). In the RSM, the classifiers are constructed in random subspaces of the data feature space. In this letter, we propose an evolved feature weighting approach: in each subspace, the features are multiplied by a weight factor for minimizing the error rate in the training set. An efficient method based on particle swarm optimization (PSO) is here proposed for finding a set of weights for each feature in each subspace. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark data sets.     

A linear discriminant analysis framework based on random subspace for face recognition

Linear discriminant analysis (LDA) often suffers from the small sample size problem when dealing with high-dimensional face data. Random subspace can effectively solve this problem by random sampling on face features. However, it remains a problem how to construct an optimal random subspace for discriminant analysis and perform the most efficient discriminant analysis on the constructed random subspace. In this paper, we propose a novel framework, random discriminant analysis (RDA), to handle this problem. Under the most suitable situation of the principal subspace, the optimal reduced dimension of the face sample is discovered to construct a random subspace where all the discriminative information in the face space is distributed in the two principal subspaces of the within-class and between-class matrices. Then we apply Fisherface and direct LDA, respectively, to the two principal subspaces for simultaneous discriminant analysis. The two sets of discriminant analysis features from dual principal subspaces are first combined at the feature level, and then all the random subspaces are further integrated at the decision level. With the discriminating information fusion at the two levels, our method can take full advantage of useful discriminant information in the face space. Extensive experiments on different face databases demonstrate its performance.     

随机采样的2DPCA人脸识别方法

在2DPCA的基础上提出一种随机采样的2DPCA人脸识别方法--RRS-2DPCA.同传统通过对特征或投影向量进行采样的方法不同的是,RRS-2DPCA(Row Random Sampling 2DPCA)将随机采样建立于图像的行向量集中,然后在行向量子集中执行2DPCA.在ORL、Yale和AR人脸数据集上进行实验,结果表明RRS-2DPCA不仅具很好的识别性能和运算效率,而且对参数具有很大的稳定性.另外针对2DPCA和RRS-2DPCA对光线、遮挡等不鲁棒问题,进一步提出了局部区域随机采样的2DPCA方法LRRS-2DPCA(Local Row Random Sampling 2DPCA),将RRS-2DPCA执行在人脸图像的局部区域中.实验结果表明LRRS-2DPCA不仅具有较好的鲁棒性更大大的提高了RRS-2DPCA的识别性能.     

一种基于2D-DWT和2D-PCA的人脸识别方法

提出了一种联合图像二维离散小波变换（2D-DWT）和二维主成分分析（2D-PCA）的人脸识别方法。首先通过2D-DWT将当前图像分解成四个子图像，其中一子图像对应原图像的主体部分（低通部分），其余三个子图像则对应图像的细节部分（高通部分）。在此基础上，采用2D-PCA方法分别对每一子图像进行特征提取。此外，文中还提出了一种简单有效的方法对各子图像中所提取的特征进行融合，根据所得到的特征进行人脸识别。同其他基于小波分解的人脸识别方法相比，所提出的方法能更充分地利用人脸图像的有用判别信息，并得到更好的识别结果。     

Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm

In this paper, a novel spectral-spatial hyperspectral image classification method has been proposed by designing hierarchical subspace switch ensemble learning algorithm. First, the hyperspectral images are processed by fast bilateral filtering to get the spatial features. The spectral features and spatial features are combined to form the initial feature set. Second, Hierarchical instance learning based on iterative means clustering method is designed to obtain hierarchical instance space. Third, random subspace method (RSM) is used for sampling the features and samples, thereby forming multiple sub sample set. After that, semi-supervised learning (S²L) is applied to choose test samples for improving classification performance without touching the class labels. Then, micro noise linear dimension reduction (mNLDR) is used for dimension reduction. Afterwards, ensemble multiple kernels SVM(EMK_SVM) are used for stable classification results. Finally, final classification results are obtained by combining classification results with voting strategy. Experimental results on real hyperspectral scenes demonstrate that the proposed method can effectively improve the classification performance apparently.     

融合2DPCA和模糊2DLDA的人脸识别

结合模糊集理论、双向二维主成分-线性鉴别分析((2D)2PCALDA)的特点,提出一种新的人脸图像特征提取方法。算法首先对人脸图像进行二维主成分分析(2DPCA)处理,再用模糊K近邻算法计算图像的隶属度矩阵,并将其融入到2DLDA过程中,从而得到模糊类间散射矩阵和模糊类内散射矩阵。与(2D2PCALDA相比,该算法充分利用了(2D)2PCALDA的优点,有效地提取了行和列的识别信息,并充分考虑了样本的分布信息。在Yale和FERET人脸数据库上的实验结果表明,该方法识别效果优于(2D)2PCALDA、双向二维主成分分析((2D)2PCA)等方法。     

Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine

In this paper, a novel recognition algorithm based on discriminant tensor subspace analysis (DTSA) and extreme learning machine (ELM) is introduced. DTSA treats a gray facial image as a second order tensor and adopts two-sided transformations to reduce dimensionality. One of the many advantages of DTSA is its ability to preserve the spatial structure information of the images. In order to deal with micro-expression video clips, we extend DTSA to a high-order tensor. Discriminative features are generated using DTSA to further enhance the classification performance of ELM classifier. Another notable contribution of the proposed method includes significant improvements in face and micro-expression recognition accuracy. The experimental results on the ORL, Yale, YaleB facial databases and CASME micro-expression database show the effectiveness of the proposed method.     

双向压缩二维特征抽取人脸识别新方法

提出了二维主成分分析(2DPCA)与二维线性鉴别分析(2DLDA)相结合的双向压缩投影的子空间人脸识别方法.该方法在进行一次2DPCA运算后,对特征矩阵进行转置,再进行2DLDA运算,与(2D)~2PCA与(2D)~2LDA相比,充分利用了2DPCA和2DLDA的优点,既包含了样本的类别信息,又消除了图像矩阵行和列的相关性,有效地提取了行和列的识别信息,识别特征维数也大幅度减少.在ORL和PERET人脸库上的实验表明,在不影响识别速度的情况下,其识别率优于现有二维特征提取方法.     

Craniofacial reconstruction based on multi-linear subspace analysis

Craniofacial reconstruction aims to estimate an individual’s facial appearance from its skull. It can be applied in many multimedia services such as forensic medicine, archaeology, face animation etc. In this paper, a statistical learning based method is proposed for 3D craniofacial reconstruction. In order to well represent the craniofacial shape variation and better utilize the relevance between different local regions, two tensor models are constructed for the skull and the face skin respectively, and multi-linear subspace analysis is used to extract the craniofacial subspace features. A partial least squares regression (PLSR) based mapping from skull subspace to skin subspace is established with the attributes such as age and BMI being considered. For an unknown skull, the 3D face skin is reconstructed using the learned mapping with the help of the skin tensor model. Compared with some other statistical learning based method in literature, the proposed method more directly and properly reflects the shape relationship between the skull and the face. In addition, the proposed method has little manual intervention. Experimental results show that the proposed method is valid.     

Random Sampling for Subspace Face Recognition

Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.     

双偏差双空间局部方向模式的人脸识别

针对局部方向数（Local Directional Number pattern,LDN）类方法的人脸识别通常仅利用梯度信息且信息提取不充分的问题,提出双偏差双空间局部方向模式（Double Variation and Double Space Local Directional Pattern,DVDSLDP）。该方法首先通过像素采样扩大关联邻域信息,再利用边缘响应算子和局部前后向差分获得的相对偏差和绝对偏差以构成双偏差信息,充分挖掘局部梯度空间信息;然后与所提取像素的灰度空间特征级联融合,以获得双空间特征,再进行模式编码得到特征图;最后依据信息熵加权级联各子块直方图获得人脸特征向量,使用最近邻分类器完成分类。针对ORL、Yale、AR人脸库和相关典型方法的对比结果表明：利用双空间特征的融合,获得了轮廓更清晰、纹理更丰富的编码特征图,在ORL和Yale库上分别达到了99.50%、94.44%的识别率,尤其是在训练样本较少时性能提升明显;该方法针对AR库的表情、光照、遮挡A和遮挡B子集分别达到了99.67%、100%、99.33%和97.33%的识别率,明显高于其他方法,表现出良好的鲁棒性。     

结合超像元和子空间投影支持向量机的高光谱图像分类

目的 高光谱图像包含了丰富的空间、光谱和辐射信息,能够用于精细的地物分类,但是要达到较高的分类精度,需要解决高维数据与有限样本之间存在矛盾的问题,并且降低因噪声和混合像元引起的同物异谱的影响。为有效解决上述问题,提出结合超像元和子空间投影支持向量机的高光谱图像分类方法。方法 首先采用简单线性迭代聚类算法将高光谱图像分割成许多无重叠的同质性区域,将每一个区域作为一个超像元,以超像元作为图像分类的最小单元,利用子空间投影算法对超像元构成的图像进行降维处理,在低维特征空间中执行支持向量机分类。本文高光谱图像空谱综合分类模型,对几何特征空间下的超像元分割与光谱特征空间下的子空间投影支持向量机（SVMsub）,采用分割后进行特征融合的处理方式,将像元级别转换为面向对象的超像元级别,实现高光谱图像空谱综合分类。结果 在AVIRIS（airbone visible/infrared imaging spectrometer）获取的Indian Pines数据和Reflective ROSIS（optics system spectrographic imaging system）传感器获取的University of Pavia数据实验中,子空间投影算法比对应的非子空间投影算法的分类精度高,特别是在样本数较少的情况下,分类效果提升明显;利用马尔可夫随机场或超像元融合空间信息的算法比对应的没有融合空间信息的算法的分类精度高;在两组数据均使用少于1%的训练样本情况下,同时融合了超像元和子空间投影的支持向量机算法在两组实验中分类精度均为最高,整体分类精度高出其他相关算法4%左右。结论 利用超像元处理可以有效融合空间信息,降低同物异谱对分类结果的不利影响;采用子空间投影能够将高光谱数据变换到低维空间中,实现有限训练样本条件下的高精度分类;结合超像元和子空间投影支持向量机的算法能够得到较高的高光谱图像分类精度。     

Unified model in identity subspace for face recognition

Human faces have two important characteristics: (1) They are similar objects and the specific variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information, is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people group A, can be generalized well to facial images of unknown individuals, and can be used to recognize facial images from an unknown people group B with only one sample per subject. Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.     

改进随机子空间LDA结合多补丁集成学习的鲁棒人脸识别算法

针对化妆对人脸识别准确率的负面影响,提出了基于补丁集成学习的改进鲁棒人脸识别算法。首先,将每张人脸图像嵌入补丁中并用一组特征描述符描述每个补丁,即本地梯度Gabor模式(LGP)、Gabor空间定序定比测量直方图(HGSFRM)和密集采样局部多值模式(DSLMP )。然后,使用改进的随机子空间线性判别分析(SRS-LDA)方法采样补丁,并在化妆之前和化妆之后图像之间建立多个公共子空间进行集成学习。最后,利用协作和稀疏表示分类器比较这个子空间中的特征向量,同时通过求和规则联合得到的分数。实验将提出的算法在多种化妆数据集上进行评估分析,结果表明提出的算法相比于其他专为妆后人脸识别设计的算法有更高的识别精度。     

Feature extraction based on Laplacian bidirectional maximum margin criterion

Maximum margin criterion (MMC) based feature extraction is more efficient than linear discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples and the structural information embedding in the images. In this paper, we develop a novel criterion, namely Laplacian bidirectional maximum margin criterion (LBMMC), to address the issue. We formulate the image total Laplacian matrix, image within-class Laplacian matrix and image between-class Laplacian matrix using the sample similar weight that is widely used in machine learning. The proposed LBMMC based feature extraction computes the discriminant vectors by maximizing the difference between image between-class Laplacian matrix and image within-class Laplacian matrix in both row and column directions. Experiments on the FERET and Yale face databases show the effectiveness of the proposed LBMMC based feature extraction method.     

基于子空间追踪的人脸识别

针对现有的基于稀疏表示的人脸识别方法没有更新优化选择的原子的问题,提出一种基于子空间追踪的人脸识别方法。在稀疏编码过程中的原子选择步骤中,引入回溯迭代优化思想和多原子选择方案,通过移除可信度较低的原子来更新优化候选支撑向量中选择的原子,使选择的原子与待识别人脸图像具有最相似的结构,从而在该原子上的稀疏编码系数具有较好的人脸重构能力。实验证明,与基于正交匹配追踪（OMP）算法和基于OMP-cholesky算法的人脸识别相比,该算法在ORL和Yale B人脸数据库上的算法复杂度较低且识别率均提高了约5%。     

Supervised Discriminant Projection with Its Application to Face Recognition

In the past few decades, many face recognition methods have been developed. Among these methods, subspace analysis is an effective approach for face recognition. Unsupervised discriminant projection (UDP) finds an embedding subspace that preserves local structure information, and uncovers and separates embedding corresponding to different manifolds. Though UDP has been applied in many fields, it has limits to solve the classification tasks, such as the ignorance of the class information. Thus, a novel subspace method, called supervised discriminant projection (SDP), is proposed for face recognition in this paper. In our method, the class information was utilized in the procedure of feature extraction. In SDP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and class information. We test the performance of the proposed method SDP on three popular face image databases (i.e. AR database, Yale database, and a subset of FERET database). Experimental results show that the proposed method is effective.