An adaptive image Euclidean distance

The image Euclidean distance (IMED) considers the spatial relationship between the pixels of different images and can easily be embedded in existing image recognition algorithms that are based on Euclidean distance. IMED uses the prior knowledge that pixels located near one another have little variance in gray scale values, and defines a metric matrix according to the spatial distance between pixels. In this paper, we propose an adaptive image Euclidean distance (AIMED), which considers not only the prior spatial knowledge, but also the prior gray level knowledge from images. The most important advantage of the proposed AIMED over IMED is that AIMED makes the metric matrix adaptive to the content of the concerned images. Two ways of using gray level information are proposed. One is based on gray level distances, and the other is based on cosine dissimilarity of gray levels. Experiments on two facial databases and a handwritten digital database show that AIMED achieves the highest classification accuracy when it is embedded in nearest neighbor classifiers, principal component analysis, and support vector machines.     

基于改进的等距离映射算法的人脸识别

针对等距离映射(Isomap)算法在处理扰动图像时拓扑结构不稳定的缺点,提出了一种改进算法。改进算法将图像欧氏距离(IMED)嵌入到等距离映射算法之中。首先引入坐标度量系数计算图像的坐标度量矩阵,通过线性变换将原始图像从欧氏距离(ED)空间转换到图像欧氏距离空间;然后计算变换空间中样本的欧氏距离矩阵,并在此基础上构建样本邻域图,得到近似测地距离矩阵;最后采用多维标度(MDS)分析算法构造样本的低维表示。对ORL和Yale人脸数据库降维并结合最近邻分类器进行实验,基于改进算法的识别率平均分别提高了5.57%和3.95%,表明与原算法相比,改进算法在人脸识别中对图像扰动具有较好的鲁棒性。     

基于多尺度中心化二值模式的人脸表情识别

现有局部二值模式(LBP) 算子存在不足: 产生的直方图维数过长、鉴别力不高、对噪声反应敏感. 针对此类问题, 提出中心化二值模式(CBP) 算子, 其优点: 1) 通过比较邻域中近邻点对, 大大降低了直方图维数; 2) 考虑中心像素点的作用并赋予其最高权重, 实现鉴别力的提高; 3) 改变LBP算子的符号函数, 明显减弱白噪声对图像的影响.此外, 为提高识别率, 将多尺度CBP(MCBP) 直方图作为人脸表征. 为增强算法对表情图像中细小变形的鲁棒性, 引入图像欧式距离(IMED) 并将其嵌入MCBP方法. 在JAFFE和Cohn-Kanade表情库的实验结果表明: 所提方法优于其它表情识别方法, IMED可增强MCBP的表情识别能力.     

基于核的图像欧氏距离人脸识别

引进了两幅图像之间的一种新的距离度量方法——图像欧氏距离,该距离是利用核函数对传统的欧氏距离进行改进而得到的。在此基础上,设计了一种新的分类识别方法——基于核的图像欧氏距离人脸识别方法,并应用于人脸识别中。为验证该算法的可行性,对人脸图像进行DCT变换得到预处理样本,并在ORL和Yale人脸库上进行多角度的比较实验。分析实验结果表明,该方法优于其它距离分类器算法。     

Parallel computation of the Euclidean distance transform on a three-dimensional image array

In a two- or three-dimensional image array, the computation of Euclidean distance transform (EDT) is an important task. With the increasing application of 3D voxel images, it is useful to consider the distance transform of a 3D digital image array. Because the EDT computation is a global operation, it is prohibitively time consuming when performing the EDT for image processing. In order to provide the efficient transform computations, parallelism is employed. We first derive several important geometry relations and properties among parallel planes. We then, develop a parallel algorithm for the three-dimensional Euclidean distance transform (3D-EDT) on the EREW PRAM computation model. The time complexity of our parallel algorithm is O(log/sup 2/ N) for an N/spl times/N/spl times/N image array and this is currently the best known result. A generalized parallel algorithm for the 3D-EDT is also proposed. We implement the proposed algorithms sequentially, the performance of which exceeds the existing algorithms (proposed by Yamada, 1984). Finally, we develop the corresponding parallel programs on both the emulated EREW PRAM model computer and the IBM SP2 to verify the speed-up properties of the proposed algorithms.     

基于局部小波变换与DCT的人脸识别算法

提出了一种基于局部小波变换和离散余弦变换(DiscreteCosineTransform,DCT)相结合的人脸识别方法,该算法首先利用小波变换对人脸图像做适当层次的小波分解,然后通过离散余弦变换对低频分量作进一步的特征提取和压缩,得到人脸识别特征,最后利用欧氏距离和最近邻分类器进行识别。基于ORL人脸数据库的实验结果表明了该算法的有效性。     

Face recognition under varying illumination using Mahalanobis self-organizing map

We present an appearance-based method for face recognition and evaluate its robustness against illumination changes. Self-organizing map (SOM) is utilized to transform the high dimensional face image into low dimensional topological space. However, the original learning algorithm of SOM uses Euclidean distance to measure similarity between input and codebook images, which is very sensitive to illumination changes. In this paper, we present Mahalanobis SOM, which uses Mahalanobis distance instead of the original Euclidean distance. The effectiveness of the proposed method is demonstrated by conducting some experiments on Yale B and CMU-PIE face databases. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

图像欧氏距离在人脸识别中的应用研究

图像欧氏距离可以嵌入到许多传统的图像分类识别算法中,该嵌入是通过对原始图像的线性变换来实现的,给出了一种基于数据场的图像线性变换方法,将其应用到图像欧氏距离中.实验结果表明,基于数据场的线性变换方法是一种可行的图像线性变换方法,该方法可以完成大尺度图像的线性变换,方便地将图像欧氏距离嵌入到传统人脸识别算法中.     

利用图像距离的监督LLE头部姿态估计方法

局部线性嵌入(LLE)是一种经典流形学习方法,直接应用这种非监督的传统LLE估计图像中的头部姿态存在两点不足:未考虑图像像素空间信息和未利用样本标记信息.因此,本文结合图像欧式距离和偏置LLE流形学习方法,对头部姿态图像降维,并通过广义回归神经网络(GRNN)和多元线性回归的方法,估计头部图像的姿态.在FacePix头部姿态数据库的对比实验表明,本方法具有较好的头部姿态估计效果.     

三维欧氏距离变换的一种新方法

常见的三维距离变换算法大都是对城市街区、棋盘等二维近似欧氏距离变换算法的三维扩展,得到的依然是近似欧氏距离.提出一种新的三维欧氏距离变换算法,可以得到完全欧氏距离,时间复杂度为O(n3logn).将该算法应用于三维医学CT图像内部软组织的显示,取得了较好的效果.     

基于小波变换与脉冲耦合神经网络的人脸识别

脉冲耦合神经网络（Pulse Coupled Neural Network,PCNN）是基于生物视觉特性而提出的新一代人工神经网络,它在数字图像处理及人工智能等领域具有广泛应用前景.本文通过研究PCNN理论模型及其工作特性的基础上提出了一种提取人脸特征的方法.首先利用小波变换提取人脸图像低频特征,降低人脸图像的维度,然后利用简化的PCNN提取小波低频系数重构后的人脸图像的相应时间序列,并以此作为人脸识别的特征序列.最后利用时间序列和欧式距离完成人脸的识别过程.本文通过ORL人脸库进行实验证明了该方法的有效性.     

基于支持向量机的人脸检测训练集增强

根据支持向量机(support vector machine,简称SVM)理论,对基于边界的分类算法(geometric approach)而言,类别边界附近的样本通常比其他样本包含有更多的分类信息.基于这一基本思路,以人脸检测问题为例,探讨了对给定训练样本集进行边界增强的问题,并为此而提出了一种基于支持向量机和改进的非线性精简集算法IRS(improved reduced set)的训练集边界样本增强算法,用以扩大训练集并改善其样本分布.其中,所谓IRS算法是指在精简集(reduced set)算法的核函数中嵌入一种新的距离度量——图像欧式距离——来改善其迭代近似性能,IRS可以有效地生成新的、位于类别边界附近的虚拟样本以增强给定训练集.为了验证算法的有效性,采用增强的样本集训练基于AdaBoost的人脸检测器,并在MIT CMU正面人脸测试库上进行了测试.实验结果表明,通过这种方法能够有效地提高最终分类器的人脸检测性能.     

Anthropometric 3D Face Recognition

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.     

Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm

One of the challenges of face recognition in surveillance is the low resolution of face region. Therefore many superresolution (SR) face reconstruction methods are proposed to produce a high-resolution face image from one or a set of low-resolution face images. However, existing dictionary learning based algorithms are sensitive to noise and very time-consuming. In this paper, we define and prove the multi-scale linear combination consistency. In order to improve the performance of SR, we propose a novel SR face reconstruction method based on nonlocal similarity and multi-scale linear combination consistency (NLS-MLC). We further proposed a new recognition approach for very low resolution face images based on resolution scale invariant feature (RSIF). A series of experiments are conducted on two public face image databases to test feasibility of our proposed methods. Experimental results show that the proposed SR method is more robust and computationally effective in face hallucination, and the recognition accuracy of RSIF is higher than some state-of-art algorithms.      

基于改进的多流形算法的人脸图像集识别

一个图像集由大量变化不一的图像组成,而且这些图像都表示同一个人.现实中的图像集数据是非线性的,造成这些现象的因素有人脸的角度不同、光线的明暗等,因此图像集中的每幅图像都是变化的,如果近似的将一个图像集建模为线性子空间,而忽略了集合中数据结构的变化,很显然是不合理的,这也必然会影响到最后的识别率.受流形理论知识的启发,可以将图像集建模为一个流形,这与传统的将图像集建模为子空间的方法有着本质区别.本文在基于流形的人脸图像集识别方法的基础上进行改进,提出新的计算样子空间距离方法,最后采用所有最短子空间距离的平均值作为流形之间的距离,称为改进的多流形方法(Improved multi-manifold method,IMM).IMM方法在CMU PIE数据库上进行实验,结果表明该方法相比其他方法具有更高识别率.     

基于小波变换和二维非负矩阵分解的人脸识别算法

结合二维离散小波变换(2DDWT)和二维非负矩阵分解(2DNMF)两者的优点,提出了一种新的人脸识别融合算法2DDWT+2DNMF。首先利用小波变换把人脸图像分解成四个子块频带区域,并对三个高频子块进行图像融合,然后对低频子块和融合图像进行二维非负矩阵分解以提取特征,进而对特征数据进行加权处理。ORL和YALE人脸数据库中的识别实验表明,与PCA、SVD、NMF以及2DDWT+NMF算法相比,新融合算法能有效缩短训练时间和提高识别率。     

基于矩-傅里叶描述子人脸图像识别

本文介绍了在Radon变换下的图像矩特征的抽取方法，并得到图像的矩特征矩阵；进而对矩特征矩阵按行向量进行傅里叶变换组成矩－傅里叶描述子特征矩阵，采用矩阵的加权欧氏距离作为人脸图像的匹配识别的算法，产生较好的结果。     

纹理特征和两级分类器相结合的人脸识别

为了提高人脸识别率和识别效率,提出一种纹理特征和两级分类器相结合的人脸识别方法。采用灰度共生矩阵表示人脸图像的纹理特征,计算待识别人脸图像与模板间欧式距离,采用拒识阈值进行评判,如果人脸图像归属类别清楚,则采用欧式距离分类器进行识别,否则将待识人脸图像送入SVM分类器进行识别,采用ORL人脸数据库和Yale人脸数据库进行仿真实验。仿真结果表明,相对于单一人脸识别器,两级分类器不仅提高了人脸识别效率,而且提高了人脸识别率,具有更好的人脸识别性能。     

A multiresolution manifold distance for invariant image similarity

Accounting for spatial image transformations is a requirement for multimedia problems such as video classification and retrieval, face/object recognition or the creation of image mosaics from video sequences. We analyze a transformation invariant metric recently proposed in the machine learning literature to measure the distance between image manifolds - the tangent distance (TD) - and show that it is closely related to alignment techniques from the motion analysis literature. Exposing these relationships results in benefits for the two domains. On one hand, it allows leveraging on the knowledge acquired in the alignment literature to build better classifiers. On the other, it provides a new interpretation of alignment techniques as one component of a decomposition that has interesting properties for the classification of video. In particular, we embed the TD into a multiresolution framework that makes it significantly less prone to local minima. The new metric - multiresolution tangent distance (MRTD) - can be easily combined with robust estimation procedures, and exhibits significantly higher invariance to image transformations than the TD and the Euclidean distance (ED). For classification, this translates into significant improvements in face recognition accuracy. For video characterization, it leads to a decomposition of image dissimilarity into "differences due to camera motion" plus "differences due to scene activity" that is useful for classification. Experimental results on a movie database indicate that the distance could be used as a basis for the extraction of semantic primitives such as action and romance.     

New MCT-based face recognition under varying lighting conditions

This paper presents a new face recognition algorithm that is insensitive to variations in lighting conditions. In the proposed algorithm, the MCT (Modified Census Transform) was embedded to extract the local facial features that are invariant under illumination changes. In this study, we also employed an appearance-based method to incorporate both local and global features. First, input facial images are transformed by the MCT and a bit string from the MCT is converted to a decimal number to generate an MCT domain image. This domain image is recognized using principle component analysis (PCA) or linear discriminate analysis (LDA). Experimental results reveal that the recognition rate of the proposed approach is better than that of conventional appearance-based algorithms by approximately 20% for the Yale B database, in the case of severe variations in illumination conditions. We also found that the proposed algorithm yields better performance for the Yale database for various face expressions, eye-wear, and lighting conditions.