Manifold-manifold distance and its application to face recognition with image sets

In this paper, we address the problem of classifying image sets for face recognition, where each set contains images belonging to the same subject and typically covering large variations. By modeling each image set as a manifold, we formulate the problem as the computation of the distance between two manifolds, called manifold-manifold distance (MMD). Since an image set can come in three pattern levels, point, subspace, and manifold, we systematically study the distance among the three levels and formulate them in a general multilevel MMD framework. Specifically, we express a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrate the distances between pairs of subspaces from one of the involved manifolds. We theoretically and experimentally study several configurations of the ingredients of MMD. The proposed method is applied to the task of face recognition with image sets, where identification is achieved by seeking the minimum MMD from the probe to the gallery of image sets. Our experiments demonstrate that, as a general set similarity measure, MMD consistently outperforms other competing nondiscriminative methods and is also promisingly comparable to the state-of-the-art discriminative methods.     

一种利用相位信息的雷达目标成像识别方法

高距离分辨雷达(HRR)的回波中蕴含丰富的目标信息,充分利用目标回波并采用恰当的特征抽取方法,可以有效地识别目标。本文提出一种利用相位信息和正则变换的目标识别方法。该方法基于雷达目标距离剖面像的幅度和相位矢量(幅相矢量),首先由各训练目标在不同方位姿态角时的幅相矢量构成综合矩阵并对之作正则变换建立正则子空间,然后将每类训练目标各方位姿态的幅相矢量向该子空间投影形成子像,取其平均结果作为该目标的库特征矢量。对未知目标,以其子像对库矢量的欧氏距离最小为分类准则,进行了识别模拟实验。     

基于子空间方法的阵列天线方向向量自适应盲估计

利用传统子空间法估计方向向量要求阵元数必须大于信号源数,要求在无线通信系统中是不现实的,因为移动用户数是很大的,为突破这一限制,本文提出了一种基于子空间法的阵列天线方向向量自适应盲估计算法.通过各阵元接收信号相加,得到叠加的期望数据源特征波形;通过子空间方法,将叠加信号用信号子空间的基表示,由于叠加信号中包含方向向量,从而得到期望数据源的方向向量估计,该算法不要求阵元数大于数据源数.仿真结果表明算法是有效的.     

Gait recognition using radon transform and linear discriminant analysis.

A new feature extraction process is proposed for gait representation and recognition. The new system is based on the Radon transform of binary silhouettes. For each gait sequence, the transformed silhouettes are used for the computation of a template. The set of all templates is subsequently subjected to linear discriminant analysis and subspace projection. In this manner, each gait sequence is described using a low-dimensional feature vector consisting of selected Radon template coefficients. Given a test feature vector, gait recognition and verification is achieved by appropriately comparing it to feature vectors in a reference gait database. By using the new system on the Gait Challenge database, very considerable improvements in recognition performance are seen in comparison to state-of-the-art methods for gait recognition.     

一种互联网宏观流量异常检测方法

网络流量异常指网络中流量不规则地显著变化。网络短暂拥塞、分布式拒绝服务攻击、大范围扫描等本地事件或者网络路由异常等全局事件都能够引起网络的异常。网络异常的检测和分析对于网络安全应急响应部门非常重要,但是宏观流量异常检测需要从大量高维的富含噪声的数据中提取和解释异常模式,因此变得很困难。文章提出一种分析网络异常的通用方法,该方法运用主成分分析手段将高维空间划分为对应正常和异常网络行为的子空间,并将流量向量影射在正常子空间中,使用基于距离的度量来检测宏观网络流量异常事件。     

Manifold Based Local Classifiers: Linear and Nonlinear Approaches

In case of insufficient data samples in high-dimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nearby training samples from the class. When such regions lie close to inter-class boundaries, the nearest neighbors of a query may lie in the wrong class, thus leading to errors in the Nearest Neighbor classification rule. The K-local hyperplane distance nearest neighbor (HKNN) algorithm tackles this problem by approximating each class with a smooth nonlinear manifold, which is considered to be locally linear. The method takes advantage of the local linearity assumption by using the distances from a query sample to the affine hulls of query’s nearest neighbors for decision making. However, HKNN is limited to using the Euclidean distance metric, which is a significant limitation in practice. In this paper we reformulate HKNN in terms of subspaces, and propose a variant, the Local Discriminative Common Vector (LDCV) method, that is more suitable for classification tasks where the classes have similar intra-class variations. We then extend both methods to the nonlinear case by mapping the nearest neighbors into a higher-dimensional space where the linear manifolds are constructed. This procedure allows us to use a wide variety of distance functions in the process, while computing distances between the query sample and the nonlinear manifolds remains straightforward owing to the linear nature of the manifolds in the mapped space. We tested the proposed methods on several classification tasks, obtaining better results than both the Support Vector Machines (SVMs) and their local counterpart SVM-KNN on the USPS and Image segmentation databases, and outperforming the local SVM-KNN on the Caltech visual recognition database.     

A method for improving the classification speed of clustering algorithms which use a Euclidean distance metric

Many pattern recognition computer programs use one of the clustering algorithm techniques. Often these algorithms use a Euclidean distance metric as a similarity measure. A scheme is proposed where both the Euclidean metric and a more simple city-block metric are utilized together to reduce overall classification time. The relation between the Euclidean and city-block distances is introduced as a scalar function. The bounds of the function are given and used to decide whether classification of each pattern vector is to be achieved by the computationally slow Euclidean distance or the faster city-block distance. The criteria is that the classification should be identical to the original Euclidean only scheme.     

基于K-L交叉熵的岭回归人脸识别

岭回归人脸识别利用正则单形的顶点对每类人脸进行多元标记,通过投影实现高维人脸特征的降维。该算法首先提取人脸图像的局部二进制（LBP）直方图特征向量,通过主成分分析（PCA）和岭回归对该特征向量进行两次降维。识别阶段利用K-L交叉熵计算标记向量和投影后特征向量的相似性,根据熵值最小原则完成对测试样本的类别判断。实验选取ORL和YALE两个标准人脸库对算法进行测试,结果表明,K-L交叉熵测度比传统的欧氏距离测度获得更高的识别率。     

Human action recognition using Pose-based discriminant embedding

Manifold learning is an efficient approach for recognizing human actions. Most of the previous embedding methods are learned based on the distances between frames as data points. Thus they may be efficient in the frame recognition framework, but they will not guarantee to give optimum results when sequences are to be classified as in the case of action recognition in which temporal constraints convey important information. In the sequence recognition framework, sequences are compared based on the distances defined between sets of points. Among them Spatio-temporal Correlation Distance (SCD) is an efficient measure for comparing ordered sequences. In this paper we propose a novel embedding which is optimum in the sequence recognition framework based on SCD as the distance measure. Specifically, the proposed embedding minimizes the sum of the distances between intra-class sequences while seeking to maximize the sum of distances between inter-class points. Action sequences are represented by key poses chosen equidistantly from one action period. The action period is computed by a modified correlation-based method. Action recognition is achieved by comparing the projected sequences in the low-dimensional subspace using SCD or Hausdorff distance in a nearest neighbor framework. Several experiments are carried out on three popular datasets. The method is shown not only to classify the actions efficiently obtaining results comparable to the state of the art on all datasets, but also to be robust to additive noise and tolerant to occlusion, deformation and change in view point. Moreover, the method outperforms other classical dimension reduction techniques and performs faster by choosing less number of postures.     

Context-aware vocabulary tree for mobile landmark recognition

This paper presents an effective approach that incorporates contextual information into vocabulary tree learning for mobile landmark recognition. For most existing mobile landmark recognition works, the context information (GPS or direction) is mainly used to reduce the search space in a heuristic and insufficient manner. Some recent work uses the context information for codebook learning but only the GPS information is explored. We propose an effective mobile landmark recognition approach which exploits both context (direction and location) and content information for vocabulary tree learning and image recognition. The proposed approach has two major contributions: (i) it proposes an information gain-based codeword discrimination learning method to evaluate the discriminative capability of each direction-aware codeword, as generated by a context-aware vocabulary tree, and (ii) it develops a context-aware image scoring technique based on an inverted file structure that speeds up the image matching process greatly. Experimental results on the NTU and San Francisco database show that the proposed method can achieve good recognition performance with fast speed.     

采用新型纹理特征的2DLDA人脸识别算法

针对现有基于纹理特征的人脸识别算法中纹理特征维数偏大且对噪声较敏感等不足,提出了用于描述人脸图像大尺度局部特征的中心四点二元模式（Center Quad Binary Pattern, C-QBP）和用于描述图像小尺度局部特征的简化四点二元模式（Simplified Quad Binary Pattern, S-QBP）两种互补的新型纹理特征。在此基础上,实现基于新型纹理特征的2DLDA人脸识别算法。首先对人脸图像进行多级分割,再对所产生的图像块提取C-QBP和S-QBP纹理特征,构建纹理特征矩阵。最后,采用2DLDA子空间学习算法实现基于新型纹理特征的人脸识别。实验结果表明,本文所提出的人脸识别算法的识别率明显高于其他基于纹理特征和子空间学习的人脸识别算法。当每一类训练样本数统一设置为5,特征维数为48×4时,在ORL人脸库上,本文所提出的人脸识别算法的识别率达98.68%;在YALE人脸库上,特征维数为48×36时,识别率达99.42%;在FERET人脸库上,特征维数为48×26时,识别率为91.73%。      

Frontalization and adaptive exponential ensemble rule for deep-learning-based facial expression recognition system

Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems.     

地物波谱组合识别模型参数的遗传优化

为了从TM影像中提取不同地物波谱信息特征，构造了地物波谱组合识别函数关系式，提出了从地物TM影像中分辨出地物的一般原则，并采用遗传算法优化确定地物波谱组合识别函数关系式中的系数。根据不同地物与函数值分布范围的对应关系，可以较容易地由TM影像作出多种不同地物的识别。实例分析表明这种地物波谱组合识别模型具有简单性、实用性和可分辨性等特点。     

Entropy-based feature selection for improved 3D facial expression recognition

Facial expressions contain most of the information on human face which is essential for human–computer interaction. Development of robust algorithms for automatic recognition of facial expressions with high recognition rates has been a challenge for the last 10 years. In this paper, we propose a novel feature selection procedure which recognizes basic facial expressions with high recognition rates by utilizing three-Dimensional (3D) geometrical facial feature positions. The paper presents a system of classifying expressions in one of the six basic emotional categories which are anger, disgust, fear, happiness, sadness, and surprise. The paper contributes on feature selections for each expression independently and achieves high recognition rates with the proposed geometric facial features selected for each expression. The novel feature selection procedure is entropy based, and it is employed independently for each of the six basic expressions. The system’s performance is evaluated using the 3D facial expression database, BU-3DFE. Experimental results show that the proposed method outperforms the latest methods reported in the literature.     

Principal components null space analysis for image and video classification.

We present a new classification algorithm, principal component null space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i," an Mi-dimensional subspace along which the class' intraclass variance is the smallest. We call this subspace an approximate null space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class' mean in the class' ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video--an action retrieval problem and abnormal activity detection.     

幅度谱差分子空间雷达自动目标识别

雷达高分辨距离像是目标的重要结构特征,其维数通常很高,造成数据可分性表达差,识别过程计算复杂度高,识别率低。为降低距离像的维数,提出一种新的距离像特征提取方法,即采用直接线性判别分析(dLDA)在距离像幅度谱差分空间进行特征提取,得dLDA幅度谱差分子空间。目标识别即在所得dLDA幅度谱差分子空间中进行。采用外场实测数据,分别训练了最小距离分类器和one-against-all支撑向量机分类器,2种分类器的识别结果均表明,该方法可显著地降低数据维数并提高识别率。     

多姿态、光照不均人脸图像的子空间人脸识别方法研究

针对人脸识别中存在的光照不均匀问题,提出了一种预处理链技术,能达到很好的光照补偿效果。为了提高多姿态、多表情、多细节人脸图像的人脸识别率,设计了一种将最近邻分类器与支持向量机相结合的分类算法（NN-SVM）,基于该分类算法提出了一种基于Gabor变换和NN-SVM的子空间人脸识别方法。在FERET和ORL两大人脸数据库中对所提方法进行性能评估,实验结果表明所提出方法能有效的解决人脸识别中光照不均匀问题,大大的提高人脸识别率,而且相比其他现存的人脸识别方法,所设计的方法具有更好、更稳定的识别效果。     

基于子空间特征向量的三维点云相似性分析

提出一种基于子空间特征向量的三维点云相似性分析算法。首先,获取两个物体的三维点云数据,并进行位置标准化。其次,利用最小子空间分割算法将两个三维点云分别分割成若干子空间。随后,计算子空间的质心到其拟合曲面的距离和夹角,并基于上述距离和夹角构成的向量空间,提取子空间特征向量。最后,通过特征向量间的相似度计算来评价两个三维点云的相似性。由于该方法将描述三维形体特征的子空间特征向量作为相似度度量的依据,所以具有数据量小、精度高的特点。实验表明,该算法能够定量地分析两个三维物体的相似性。     

基于多线性核主成分分析的掌纹识别

提出运用多线性核主成分分析(MKPCA)的一种新方法进行掌纹识别.首先MKPCA通过非线性变换,将输入样本图像向高维特征空间F上投影,运用多线性主成分分析(MPCA)直接对掌纹张量进行降维,得到低维的投影张量;然后掌纹图像向张量子空间上投影提取特征向量;最后计算特征向量间的余弦距离进行掌纹匹配.运用PolyU掌纹图像库...