一种基于重采样双向2DLDA融合的人脸识别算法

针对人脸识别中普遍存在的光照、表情等变化带来的识别问题和小样本问题,本文提出了一种利用重采样技术融合双向2DLDA特征的人脸识别算法Resampling Bidirection 2DLDA(RB2DLDA).二维线性判别分析中,2DLDA利用垂直方向上的类内和类间协方差信息进行识别,E2DLDA利用水平方向上的类内和类...     

基于概率神经网络优化DWT-2DLDA的高噪声人脸识别

针对人脸识别中的原始图像存在噪声而影响识别性能的问题,提出了基于概率神经网络优化二维子空间分析的人脸识别方法。首先,使用离散小波变化对图像进行预处理;然后,利用二维线性判别分析进行特征提取;最后,利用概率神经网络完成人脸分类。在ORL和FEI量大通用人脸数据库及自己搜集的数据库上的实验结果表明,在添加噪声的情况下,识别率也可高达98.9%,相比几种较新的识别方法,本文方法取得更好的识别性能。     

2DPCA-ICA算法在人脸识别中的应用

传统独立元分析(Independent Component Analysis,ICA)用于人脸识别首先是将人脸图像矩阵转换成向量求白化矩阵,然后利用快速固定点算法求分离矩阵,获得人脸图像独立基子空间,从而实现人脸识别.二维主元分析(Two-dimensional Principle Component Analysis,2DPCA)无须将人脸图像矩阵转换成向量,直接利用二维人脸图像矩阵求协方差矩阵,其特征值与特征向量的计算得到简化.本文结合2DPCA与ICA算法的特点,提出2DPCA-ICA人脸识别算法.该方法通过2DPCA算法计算白化矩阵;接着利用ICA算法获得人脸图像的独立元;然后构造独立基子空间;最后依据测试样本在独立基子空间上的投影特征实现人脸识别.基于ORL与Yale人脸数据库的实验结果表明,2DPCA-ICA算法正确识别率与识别效率均高于PCA-ICA算法与2DPCA算法,是一种有效的人脸识别方法.     

基于色彩和纹理特征融合的模糊人脸识别方法

基于纹理特征的方法被广泛应用于人脸识别。然而纹理特征依赖于图像的高频细节信息,当图像出现模糊时,单纯利用纹理特征的识别方法的识别精度会急剧下降。为了克服纹理特征的在模糊人脸识别中的不足,提出了一种基于色彩特征和纹理特征融合的识别方法。首先参照人类的对立色感知机制提取人脸的色彩特征;然后,将该色彩特征和纹理特征分别用于识别分类;最后,将二者的识别相似度进行融合,得到最终的识别结果。该色彩特征描述了图像的低频信息,其对图像模糊不敏感,并且与描述图像高频信息的纹理特征具有良好的互补性。在FERET 和AR 人脸库上的实验表明,融合色彩特征和纹理特征有效地提高了模糊人脸的识别精度。     

模糊二维线性鉴别分析算法

在二维线性鉴别分析的基础上,引入了模糊数学的思想,给出了模糊二维线性鉴别分析方法。模糊二维线性鉴别分析通过隶属度函数将样本归入所有的类别之中,根据隶属度重新定义了类间散布矩阵和类内散布矩阵,进而将样本的原始分布信息通过相应的隶属度函数完全融入到最后提取的特征中。通过在ORL和耶鲁人脸库上进行的实验,结果证明了基于模糊二维线性鉴别分析方法的优越性。     

分块双向2 DPCA融合局部特征的人脸识别

针对(2D)2PCA无法保存某些重要局部特征的问题,提出了一种分块双向二维主成分分析融合局部特征方法。首先,将图像分解为互不重叠的子块,每个子块包含重要的局部信息,利用(2D)2PCA对子块进行特征提取并投影到特征子空间;然后,对每个子块分别设计一个分类器并在一定置信度范围内判别测试样本所属类别。最后,根据所有子块所属类别的置信度之和完成人脸分类。在四个人脸识别数据库上的实验结果表明,相比其它几种人脸识别算法,所提方法取得了更高的识别精度。     

基于QR分解与2DLDA的单样本人脸识别

提出了一种新的基于矩阵的QR分解与2DLDA的单样本人脸识别算法(QR decomposition+2DLDA).利用矩阵的QR分解,将单样本人脸图像进行QR分解后提取有效的部分信息构成虚拟图像,结合原训练图像生成新的训练样本集,应用2DLDA进行特征提取和识别.在ORL人脸数据库上对算法进行了实验,实验结果表明此算法的识别效果不仅优于PCA、SPCA、(PC)2 A、E(PC)2 A算法,而且对于光照、表情等因素具有良好的鲁棒性.     

基于LDA算法的人脸识别方法的比较研究

线性判别分析（LDA）是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人脸识别会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于LDA的人脸识别。文章对几种基于LDA的人脸识别方法做了理论上的比较和实验数据的支持，这些方法包括Eigenfaces、Fisherfaces、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法。     

基于子模式双向二维线性判别分析的人脸识别

针对表情和光照变化等对人脸识别影响的问题,提出一种基于子模式双向二维线性判别分析(Sub-pattern two-directional two-dimensional linear discriminant analysis,Sp-(2D)2 LDA)的人脸识别方法。该方法首先对原图像进行分块处理,并保持子块间的空间关系,然后对各个子训练样本集从行方向和列方向同时利用2DLDA进行特征抽取,最后把各个子特征矩阵拼接成一对应原始图像的特征矩阵,并采用最近邻分类器进行分类识别。在ORL及Yale人脸库上的试验结果表明,Sp-(2D)2 LDA有效降低了鉴别特征的维数,减少了表情和光照变化的影响,获得了较好的识别性能。     

基于Gabor小波和改进2DPCA的人脸识别方法

在人脸识别中,一般传统方法先用Gabor小波变换提取人脸特征后直接进行主成分分析(PCA)会遇到计算量过大,识别率不高等问题。为了克服这些影响,本文提出先对人脸进行2DGabor小波变换提取人脸特征,再用改进的2DPCA(二维主成分分析)进行降维处理,最后用最近邻法进行分类识别。在ORL人脸库上进行实验,该方法明显优于传统的2DGabor+PCA等算法。     

Color local texture features for color face recognition

This paper proposes new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face region. Furthermore, in order to maximize a complementary effect taken by using both color and texture information, the opponent color texture features that capture the texture patterns of spatial interactions between spectral channels are also incorporated into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework. Extensive and comparative experiments have been conducted to evaluate our color local texture features for FR on five public face databases, i.e., CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that FR approaches using color local texture features impressively yield better recognition rates than FR approaches using only color or texture information. Particularly, compared with grayscale texture features, the proposed color local texture features are able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as for small- (low-) resolution face images. In addition, the feasibility of our color local texture features has been successfully demonstrated by making comparisons with other state-of-the-art color FR methods.     

色度与亮度特征相融合的彩色人脸识别算法

在存在色度与亮度差异性的条件下,彩色人脸的识别准确度不高,为了提高彩色人脸的识别性能,提出基于色度与亮度特征相融合的彩色人脸识别算法。对采集的原始彩色人脸图像进行色度均衡处理,采用特征提取方法进行人脸的关键特征点定位,提取人脸图像的色度和亮度特征。对提取的特征量根据眼睛和眉毛等关键点的差异性进行信息融合,对提取的眼部区域色度信息进行二值化分离和信息增强处理,提高对彩色人脸图像的特征识别能力,实现彩色人脸识别算法的改进设计。仿真结果表明,采用该方法进行彩色人脸识别的准确识别率较高,抗色度和亮度的局部干扰能力较强。     

基于探测过程建模的探地雷达多目标识别

在探地雷达探测过程中,天线相对目标的远近变化反映在面向深度的一维时域信号（A-scan）所组成的序列的变化过程中,由此提出一种针对变化过程建模的目标识别方法。在特征提取环节,提出将时频分析与图像纹理分析相结合,首先计算A-scan信号的二维时频联合分布图像,再利用特定的图像纹理描述算子构造特征向量。识别过程根据目标与天线间距离的变化,采用无跨越单向连续隐马尔可夫模型（HMM）对序列的变化过程建模。实验表明这种基于变化过程的HMM方法比无序地利用单条A-scan特征的支持向量机方法具有更好的效果。     

Efficient radar target recognition using the MUSIC algorithm andinvariant features

An efficient technique is developed to recognize target type using one-dimensional range profiles. The proposed technique utilizes the Multiple Signal Classification algorithm to generate superresolved range profiles. Their central moments are calculated to provide translation-invariant and level-invariant feature vectors. Next, the computed central moments are mapped into values between zero and unity, followed by a principal component analysis to eliminate the redundancy of feature vectors. The obtained features are classified based on the Bayes classifier, which is one of the statistical classifiers. Recognition results using five different aircraft models measured at compact range are presented to assess the effectiveness of the proposed technique, and they are compared with those of the conventional range profiles obtained by inverse fast Fourier transform     

基于二维图像表示的人脸识别算法研究

尽管传统的人脸识别技术已经有了非常大的进步,在很多方面仍然存在一定的缺陷。文章对二维图像进行了深层次的分析,从二维图像的特征、研究意义以及应用范围进行讨论,再结合人脸识别技术和算法从4个不同的角度切入,对二维图形表示的人脸识别算法进行了详细阐述。     

An improved LBP algorithm for texture and face classification

Local Binary Pattern (LBP) has achieved great success in texture classification due to its accuracy and efficiency. Traditional LBP method encodes local features by binarying the difference in local neighborhood and then represents a given image using the histogram of the binary patterns. However, it ignores the directional statistical information. In this paper, some directional statistical features—including the mean and standard deviation of the local absolute difference—are integrated into the feature extraction to improve the classification ability of the extracted features. In order to reduce estimation errors of the local absolute difference, we further utilize the least square estimate technique to optimize the weight and minimize the local absolute difference, which leads to more stable directional features. In addition, a novel rotation invariant texture classification approach is presented. Experimental results on several texture and face datasets show that the proposed approach significantly improves the classification accuracy of the traditional LBP.     

适于人脸识别的PCA算法应用研究

人脸识别问题涵盖了多个领域如图像处理、计算机视觉和模式识别等,文中主要研究了PCA算法的原理,并基于MATLAB软件平台实现了人脸识别系统。本系统对图像进行预处理后,用ORL人脸库中的部分图像作为样本,通过K-L变换获取训练样本的特征值和特征向量,从而得到特征脸向量,然后将测试样本投影到已知的特征脸空间,计算出坐标系数,进而得出分类识别的结果。实验证明了本系统有较高的人脸识别率,具有一定的实际应用价值。     

Research on a face recognition algorithm based on 3D face data and 2D face image matching

Under the condition of weak light or no light, the recognition accuracy of the mature 2D face recognition technology decreases sharply. In this paper, a face recognition algorithm based on the matching of 3D face data and 2D face images is proposed. Firstly, 3D face data is reconstructed from the 2D face in the database based on the 3DMM algorithm, and the face depth image is obtained through orthogonal projection. Then, the average curvature map of the face depth image is used to enhance the data of the depth image. Finally, an improved residual neural network based on the depth image and curvature is designed to compare the scanned face with the face in the database. The method proposed in this paper is tested on the 3D face data in three public face datasets (Texas 3DFRD, FRGC v2.0, and Lock3DFace), and the recognition accuracy is 84.25%, 83.39%, and 78.24%, respectively.