基于韦伯梯度方向直方图的人脸识别算法

针对传统人脸识别算法在姿态、表情和光照等变化下而引起识别效果不佳的问题,提出一种韦伯梯度方向直方图人脸识别算法（HWOG）。利用差动激励提取图像的结构和纹理信息,利用HOG算子提取原始图像的边缘特征,分块统计直方图特征信息,将所有分块的直方图串接得到人脸图像HWOG特征,用最近邻分类器进行分类。在YALE人脸库、ORL人脸库上和CAS-PEAL-R1进行实验,实验结果表明所提算法能有效提高识别率,且对光照、表情和姿态变化有较好的鲁棒性。     

一种新融合算法的维吾尔族人脸识别

针对维吾尔族人脸在光照以及部分遮挡下的辨识率下降和鲁棒性差的问题,提出了二维离散余弦变换（2DDCT）与方向边缘幅值模式（POEM）相融合的维吾尔族人脸识别算法。首先,把维吾尔族人脸图像分块处理,并使用2DDCT把其分块后的维吾尔族人脸图像转换为频域状态;其次,压缩维吾尔族人脸图像以排除维吾尔族人脸图像中无用信息,即中频部分与非低频部分,并进行二维离散余弦逆变换（IDCT）得到重构的维吾尔族人脸图像;然后,经POEM计算维吾尔族人脸图像的特征量得到其相应的POEM直方图并把直方图级联在一起,作为该中心特征点的POEM纹理直方图,得到维吾尔族人脸特征点的纹理特征信息;最后,采用深度学习算法进行分类识别。本文通过实验提出的算法,在自建的维吾尔族人脸库中能够进一步提高其人脸识别率,在维吾尔族人脸数据库中其运算速度也有很大提高。实验结果表明,该算法尤其是在维吾尔族人脸数据库中拥有较好的识别精度,具有很强的鲁棒性,特别是在光照以及部分遮挡下具有很强的优势。     

类特定字典学习-局部核协同表示分类的人脸识别法

针对人脸识别中的光照变化和遮挡等固有难题,提出一种核函数和分块相结合的人脸识别方法。对人脸图像分块,利用高斯核映射各子块到更高维空间;结合类特定字典学习得到各子块对应的局部核协同表示的每类重构误差;根据重构误差的倒数以投票完成从人脸局部到全局的识别。在Extend Yale B,AR,CMU PIE人脸库上的实验表明:提出的方法不仅具有较高的识别正确率,同时对光照变化以及遮挡的人脸图像具有较强的鲁棒性。     

信息熵加权的HOG特征提取算法研究

针对人脸图像中不同部位所含的信息熵不同,对识别的影响程度不同等因素,提出了一种信息熵加权的HOG特征提取方法。该算法将待识别的人脸图像进行分块,对分块后的图像进行HOG特征提取,计算每块图像所含的信息熵作为权重系数加到各个分块中形成新的HOG特征,通过PCA算法对特征进行降维,得到信息熵加权的HOG特征。通过在ORL和YALE实验结果表明,该算法相较于其他传统识别方法具有更高的识别精度和准确度,并且对于人脸在光照、姿态表情等干扰因素下均具有良好的有效性和鲁棒性。     

基于改进局部五值模式的人脸识别

针对局部五值模式EQP(Elongated Quinary Pattern)采用全局阈值定义造成对图像灰度变化敏感以及在人脸识别中对图像不同分块同等对待问题,提出基于局部五值模式增强方法。首先,通过自适应方法来设置阈值,以提高其对图像灰度变化的鲁棒性;其次,通过特征块加权处理,融入每个分块结构对比信息,以突出不同分块的不同作用。采用在人脸识别领域广泛应用的ORL与YALE人脸库进行比较实验,实验结果表明,新方法明显提高了EQP算子的识别效果。     

基于分块二维线性鉴别分析的人脸识别

基于2DLDA方法,提出了一种基于图像分块的二维线性鉴别分析(M2DLDA)的人脸识别方法。该方法首先对原始人脸图像进行必要的预处理后进行分块,再对分块后的子图像分别采用2DLDA方法进行特征提取,最后用最小距离分类器进行识别。该方法的优点：分块后能有效的抽取人脸图像的局部特征有利于分类;降低了2DLDA方法提取的特征矩阵的维数;特征提取是基于图像矩阵的,抽取方便快速。在ORL人脸数据库上的实验结果表明：该方法在识别性能上优于2DLDA方法。     

融合小波变换与KPCA的分块人脸特征抽取与识别算法

鉴于小波多尺度变换对高维图像特征具有良好的压缩和表达能力,提出了一种融合小波变换与KPCA(核主成分分析)方法的分块人脸特征抽取与识别算法。该算法首先对人脸图像进行分块小波变换,再根据图像块的位置分布选取不同的频率分量;然后对此分量进行KPCA特征抽取,并通过对抽取到的特征进行融合来得到最终人脸鉴别特征;最后利用支持向量机分类器进行特征分类与识别。通过对ORL和Yale标准人脸图像库的实验仿真结果表明,该算法不仅在识别性能和分类速度上明显高于传统的PCA方法及融合小波特征的KPCA方法,而且对于人脸光照、姿态和表情变化均具有良好的鲁棒性。     

基于分块稀疏表示的部分遮挡人脸识别

人脸识别利用已有的人脸数据库对给定的人脸图像进行识别,不同光照、不同姿态、部分遮挡情况下的识别问题是其中的热门研究课题。笔者针对人脸图像中的部分遮挡问题,通过AR人脸库对处理过的人脸图像采用分块稀疏表示的方法,在不同训练样本下进行多次实验。实验结果表明,分块稀疏表示对部分遮挡人脸的识别率有较大提升。     

采用LBP金字塔的人脸描述与识别

为了有效地提取人脸图像的全局和局部特征以提高人脸识别的性能,提出一种基于LBP金字塔特征的人脸描述与识别算法.首先通过多尺度分析构建人脸图像金字塔;然后采用LBP算子提取各层图像的LBP特征谱,建立图像的LBP金字塔;最后对LBP金字塔各层特征谱进行分块统计,并将各层的统计直方图序列连接起来作为人脸的鉴别特征用于分类识别.该算法在ORL和FERET人脸数据库上取得了较高的人脸识别率.实验分析表明,LBP金字塔特征具有较强的人脸描述能力和可鉴别性,且对光照、人脸表情及位置的变化具有较高的鲁棒性.     

基于单张人脸图像三维形状模型重建

在人脸图像识别优化的研究中,针对由单张人脸图像重建三维模型时对人脸图像姿态存在要求的问题,为了提高识别精度,提出基于单张人脸图像姿态预估计和主成分分析(PCA)的形状模型重建算法.首先由三维姿态估计方法得到人脸姿态,并建立人脸形状模型样本库,然后通过选取的特征点,利用主成分分析进行三维人脸形状模型的重构,最后利用径向基函数(RBF)变换和特征点坐标精确调整三维人脸形状模型,并进行仿真.仿真结果表明,重构的三维人脸形状模型效果良好,提高了精度,对有旋转姿态的人脸图像和特征点定位误差也有很好的鲁棒性.     

抗压缩的照片图像与真实感计算机图形识别

数字图像取证是计算机取证、信息安全领域的一门新学科。为实现照片图像与真实感计算机图形的可靠识别,提出一种基于图像稀疏表示的数字图像取证方法,该方法在抵抗压缩方面具有较好性能,从而保证图像压缩不会改变照片图像与真实感计算机图形的真实性本质。Tetrolet变换为保护图像局部几何结构,在L1-范数最小约束下搜索4×4图像块的最优覆盖(Covering)形式,获得图像的稀疏表示。观察自适应值c的统计分布,得到一幅图像中117种Covering出现次数的归一化直方图,从而得到图像的HoC(histogram of covering)特征。实验结果表明,在饱和度(S)分量提取的HoC特征能够很好地刻画照片图像与真实感计算机图形在局部几何结构上的不同统计特性,算法在识别能力、泛化能力,尤其是抵抗压缩能力上表现出良好性能,能够应用于图像真实性检测及照片图像与计算机图形的自动分类。     

一种新的全变分块匹配声呐图像降噪算法研究

乘性斑点噪声广泛存在于声呐图像中,严重影响图像质量,该噪声服从瑞利分布 特性。为此,结合基于全变分算法与三维块匹配图像降噪算法(BM3D)设计思路,提出了一种新 的全变分块匹配声纳图像降噪算法。首先对含噪声呐图像利用 2-范数进行块匹配分组;其次由 于声呐图像模糊、纹理细节信息较少等特点,用全变分算法对分组后的图像块进行滤波降噪; 最后对滤波后的图像块进行加权聚类得到降噪后图像。经过实验结果显示,该算法相对于经典 的 Lee 滤波、Frost 滤波、BM3D 和全变分算法有更好的降噪效果。     

Sparse L1-norm-based linear discriminant analysis

Linear discriminant analysis (LDA) is a well-known feature extraction method, which has been widely used for many pattern recognition problems. However, the objective function of conventional LDA is based on L2-norm, which makes LDA sensitive to outliers. Besides, the basis vectors learned by conventional LDA are dense and it is often hard to explain the extracted features. In this paper, we propose a novel sparse L1-norm-based linear discriminant analysis (SLDA-L1) which not only replaces L2-norm in conventional LDA with L1-norm, but also use the elastic net to regularize the basis vectors. Then L1-norm used in SLDA-L1 is for both robust and sparse modelling simultaneously. We also propose an efficient iterative algorithm to solve SLDA-L1 which is theoretically shown to arrive at a locally maximal point. Experiment results on some image databases demonstrate the effectiveness of the proposed method.     

Discriminative sparse representation for face recognition

Recently Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition. In SRC, the testing image is expected to be best represented as a sparse linear combination of training images from the same class, and the representation fidelity is measured by the ?₂-norm or ?₁-norm of the coding residual. However, SRC emphasizes the sparsity too much and overlooks the spatial information during local feature encoding process which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the spatial information but overlooks the different discriminative ability in different face regions. In this paper, we propose to weight spatial locations based on their discriminative abilities in sparse coding for robust face recognition. Specifically, we learn the weights at face locations according to the information entropy in each face region, so as to highlight locations in face images that are important for classification. Furthermore, in order to construct a robust weights to fully exploit structure information of each face region, we employed external data to learn the weights, which can cover all possible face image variants of different persons, so the robustness of obtained weights can be guaranteed. Finally, we consider the group structure of training images (i.e. those from the same subject) and added an ?_2,1-norm (group Lasso) constraint upon the formulation, which enforcing the sparsity at the group level. Extensive experiments on three benchmark face datasets demonstrate that our proposed method is much more robust and effective than baseline methods in dealing with face occlusion, corruption, lighting and expression changes, etc.     

Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation

In this article, we propose a novel unsupervised change detection method for synthetic aperture radar (SAR) images. First, we generate a difference image as a weighted average of a log-ratio image and a mean-ratio image, which has the advantage of enhancing the information of changed regions and restraining the information of unchanged background regions simultaneously. Second, we propose a variational soft segmentation model based on non-differentiable curvelet regularization and L1-norm fidelity. Numerically, by using the split Bregman technique for curvelet regularization term and reformulating the L1-norm fidelity as weighted L2-norm fidelity, we get an effective algorithm in which each sub-problem has a closed-form solution. The numerical experiments and comparisons with several existing methods show that the proposed method is promising, with not only high robustness to non-Gaussian noise or outliers but also high change detection accuracy. Moreover, the proposed method is good at detecting fine-structured change areas. Especially, it outperforms other methods in preserving edge continuity and detecting curve-shaped changed areas.     

Face recognition using elastic local reconstruction based on a single face image

In this paper, we propose a new face recognition algorithm based on a single frontal-view image for each face subject, which considers the effect of the face manifold structure. To compare two near-frontal face images, each face is considered a combination of a sequence of local image blocks. Each of the image blocks of one image can be reconstructed according to the corresponding local image block of the other face image. Then an elastic local reconstruction (ELR) method is proposed to measure the similarities between the image block pairs in order to measure the difference between the two face images. Our algorithm not only benefits from the face manifold structure, in terms of being robust to various image variations, but also is computationally simple because there is no need to build the face manifold. We evaluate the performance of our proposed face recognition algorithm with the use of different databases, which are produced under various conditions, e.g. lightings, expressions, perspectives, with/without glasses and occlusions. Consistent and promising experimental results were obtained, which show that our algorithm can greatly improve the recognition rates under all the different conditions.     

基于主梯度编码局部二进制模式的花粉图像识别

受显微传感器和不规则收集方法的影响，花粉图像常受到不同程度的噪声干扰且有着不同角度的旋转变化，识别精度普遍不高，为此提出了基于主梯度编码的局部二进制模式（DGLBP）描述子，并应用于花粉图像的分类识别。首先，计算图像块在主梯度方向上的梯度幅值；其次，分别计算图像块的径向、角向，以及复合梯度差；然后，根据各图像块的梯度差进行二进制编码，参照各局部区域的纹理分布情况为二进制编码自适应分配权重，并提取花粉图像在3个方向上的纹理特征直方图；最后，对不同尺度下的纹理特征直方图进行融合，采用欧氏距离计算各图像的相似度。DGLBP方法在Confocal和Pollenmonitor数据集上的平均正确识别率分别为94.33%和92.02%，与其他花粉识别方法相比平均提高了8.9个百分点和8.6个百分点，与LBP改进方法相比平均提高了18个百分点和18.5个百分点。实验结果表明，DGLBP描述子对花粉图像的噪声干扰和旋转变化具有较好的鲁棒性，且具有较优的识别效果。     

Document image binarization using background estimation and stroke edges

Document images often suffer from different types of degradation that renders the document image binarization a challenging task. This paper presents a document image binarization technique that segments the text from badly degraded document images accurately. The proposed technique is based on the observations that the text documents usually have a document background of the uniform color and texture and the document text within it has a different intensity level compared with the surrounding document background. Given a document image, the proposed technique first estimates a document background surface through an iterative polynomial smoothing procedure. Different types of document degradation are then compensated by using the estimated document background surface. The text stroke edge is further detected from the compensated document image by using L1-norm image gradient. Finally, the document text is segmented by a local threshold that is estimated based on the detected text stroke edges. The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.     

基于l_(2,1)范数原子选择的图像分块稀疏重构

针对图像压缩采样中原子的选择规则难以确定的问题,在改进的正交匹配追踪算法的基础上提出了一种基于l_(2,1)范数的原子选择方式。l_(2,1)范数的原子选择方式考虑了原子间的相关性,剔除了干扰原子,选择出了代表性原子。将所提方法用于图像分块重构,算法以图像进行分块,利用l_(2,1)范数选择对图像块支撑集进行筛选,增强块特征的判别性,提高原子的稀疏度,最终提高图像重构的准确率和速率。实验结果表明,相同条件下在保证重建速度的同时,所提新方法提高了图像重构精度。     

L1范局部线性嵌入

数据降维问题存在于包括机器学习、模式识别、数据挖掘等多个信息处理领域。局部线性嵌入（LLE）是一种用于数据降维的无监督非线性流行学习算法,因其优良的性能,LLE得以广泛应用。针对传统的LLE对离群（或噪声）敏感的问题,提出一种鲁棒的基于L1范数最小化的LLE算法（L1-LLE）。通过L1范数最小化来求取局部重构矩阵,减小了重构矩阵能量,能有效克服离群（或噪声）干扰。利用现有优化技术,L1-LLE算法简单且易实现。证明了L1-LLE算法的收敛性。分别对人造和实际数据集进行应用测试,通过与传统LLE方法进行性能比较,结果显示L1-LLE方法是稳定、有效的。