基于Gabor的特征提取算法在人脸识别中的应用

针对人脸识别中的特征提取问题,提出一种新的基于Gabor的特征提取算法,利用Gabor小波变换良好的提取区分能力和LDA所具有的判别性优势来进行特征提取。首先利用Gabor小波变换来提取人脸特征。然后对得到的高维特征采用PCA进行初次降维,再利用LDA实现再次降维,得到最终的特征向量。在ORL和YALE人脸库上的实验验证了该算法的有效性。     

Face recognition using message passing based clustering method

Traditional subspace analysis methods are inefficient and tend to be affected by noise as they compare the test image to all training images, especifically when there are large numbers of training images. To solve such problem, we propose a fast face recognition (FR) technique called APLDA by combining a novel clustering method affinity propagation (AP) with linear discriminant analysis (LDA). By using AP on the reduced features derived from LDA, a representative face image for each subject can be reached. Thus, our APLDA uses only the representative images rather than all training images for identification. Obviously, APLDA is much more computationally efficient than Fisherface. Also, unlike Fisherface who uses pattern classifier for identification, APLDA performs the identification using AP once again to cluster the test image into one of the representative images. Experimental results also indicate that APLDA outperforms Fisherface in terms of recognition rate.     

High-dimensional feature extraction using bit-plane decomposition of local binary patterns for robust face recognition

Transforming an original image into a high-dimensional (HD) feature has been proven to be effective in classifying images. This paper presents a novel feature extraction method utilizing the HD feature space to improve the discriminative ability for face recognition. We observed that the local binary pattern can be decomposed into bit-planes, each of which has scale-specific directional information of the face image. Each bit-plane not only has the inherent local-structure of the face image but also has an illumination-robust characteristic. By concatenating all the decomposed bit-planes, we generate an HD feature vector with an improved discriminative ability. To reduce the computational complexity while preserving the incorporated local structural information, a supervised dimension reduction method, the orthogonal linear discriminant analysis, is applied to the HD feature vector. Extensive experimental results show that existing classifiers with the proposed feature outperform those with other conventional features under various illumination, pose, and expression variations.     

基于整体特征的人脸识别方法的研究

分析了传统的基于整体特征的人脸识别方法(如PCA和FLD等)的原理及其局限性,提出将增强型Fisher判别(EFM)应用于人脸识别中。基于EFM的人脸识别的实验取得了良好的实验结果,与PCA和FLD等方法相比优势明显,并适合于大型人脸数据库的识别任务。     

An LBP-based multi-scale illumination preprocessing method for face recognition

It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry re...     

Local maximal margin discriminant embedding for face recognition

In this paper, a manifold learning based method named local maximal margin discriminant embedding (LMMDE) is developed for feature extraction. The proposed algorithm LMMDE and other manifold learning based approaches have a point in common that the locality is preserved. Moreover, LMMDE takes consideration of intra-class compactness and inter-class separability of samples lying in each manifold. More concretely, for each data point, it pulls its neighboring data points with the same class label towards it as near as possible, while simultaneously pushing its neighboring data points with different class labels away from it as far as possible under the constraint of locality preserving. Compared to most of the up-to-date manifold learning based methods, this trick makes contribution to pattern classification from two aspects. On the one hand, the local structure in each manifold is still kept in the embedding space; one the other hand, the discriminant information in each manifold can be explored. Experimental results on the ORL, Yale and FERET face databases show the effectiveness of the proposed method.     

最大边缘准则图形嵌入在人脸识别中的应用

局部线性嵌入是一种有效地非线性维数约减方法,它能保持降维后的数据与原空间有相同的拓扑关系。但是这种方法在降维处理、可视化以及数据分类方面应用不是很广泛,针对上述问题,提出了一种新的、有效的降维以及数据分类方法——基于最大边缘准则图形嵌入方法。该方法首先构建最近邻关系图聚合数据点之间的最近邻样本,同时最大化类间间隔,保证不同类之间数据可分性大,从而更好地实现数据分类。最后,该方法的有效性分别在ORL及Yale两大人脸库上得到了验证。     

基于核局部Fisher判别分析的掌纹识别

运用核局部Fisher判别分析(KLFDA)进行掌纹识别。为了解决小样本图像识别中特征方程矩阵的奇异性问题,首先运用图像下抽样方法降低掌纹空间的维数,在低维图像上应用KLF-DA提取低维的投影向量;然后将训练图像和待识别图像的核矩阵向投影向量上投影,得到非线性局部判别特征;最后计算特征向量间的余弦距离,进行掌纹匹配。运用PolyU掌纹图像库对算法进行测试,实验结果表明,与主元分析(PCA)、Fisher判别分析(FDA)、独立元分析(ICA)、核主元分析(KPCA)和局部Fisher判别分析(LFDA)相比,本文算法的识别率(RR)最高为99%,特征提取和匹配总时间0.031 s,满足实时系统的要求。     

采用CASPCM模型进行姿势鲁棒性人脸识别

针对ASPCM模型处理转动角度较大的人脸图像时出现的不足,提出CASPCM模型.以样本与模型中心的距离为依据将训练样本分组,为每个分组训练ASPCM模型;将局部ASPCM模型的合成映射结果加权平均得到CASPCM模型的合成结果;提出利用梯度下降法使分解映射的姿势估计逐步精确.采用精确性和概括性两个标准衡量该模型的分解性能和合成性能.实验表明,CASPCM模型的分解性能和合成性能均优于ASPCM模型;基于该模型的人脸识别系统在处理转动角度较大的人脸图像时,识别率比 ASPCM模型高7%.     

基于二维Fisher线性判别的人脸耳组合识别

针对人脸易受到年龄、表情等影响,提出了脸和耳相结合的组合识别方法。利用二维Fisher线性判别(2DFLD)方法分别进行了脸、耳图像层和特征层的组合识别。在北京科技大学人耳库和ORL人脸库上进行实验,结果表明,图像层组合和特征层组合的识别率分别为97.5%、95.0%,分别比人脸识别提高了12.5%和10.0%,比人耳识别提高了5.0%和2.5%;与同样应用于组合识别的主成分分析(PCA)、二维PCA(2DPCA)比较,也取得了较好识别效果。这说明,多生物特征组合识别是一种有效的识别方法。     

局部线性判别分析及其在掌纹识别中的应用

在线性判别分析(LDA)算法基础上强调图像类间数据的局部可分性,提出一种称为局部LDA(LLDA)的新子空间方法,并给出LLDA的图嵌入表示。针对LLDA同样存在的小样本问题,首先给出了传统适于LDA的主成分分析(PCA)预处理方法不适于LLDA算法的证明;进而提出了基于散度差判别准则(SDDC)的LLDA(SLLDA),既克服了LLDA的小样本问题,又提供了真实比较LLDA和LDA的平台。在PolyU掌纹数据库上的实验结果表明本文提出的SLLDA算法用于识别的有效性,也验证了数据局部关系对分类的重要性。     

基于判别核窗宽的掌纹识别方法

提出了一种新的判别 核窗宽方法,进而研究了基于判别核窗宽的KPCA和LPP在掌纹识别中的应用。首先根据训练 样本和类标签计 算类内核窗宽和类间核窗宽;在分类密集区选择较小窗宽,在分类稀疏区选择较大窗宽,可 以有效提取数 据的关联特征;然后运用基于判别核窗宽的KPCA和LPP方法提取低维特征向量,计算特征向 量间的余弦 距离进行掌纹匹配;最后运用PolyU掌纹图像库,对本文算法进行测试。实验结果表明,与 传统算法相比, 本文算法的识别率最高,识别时间小于0.6s,验证了方法的有效性 。     

Classifier fusion based on evidence theory and its application in face recognition

A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL), which is called Cen-ter-based Nearest Neighbor (CNN). CNN retains the advantages of NFL while it has relatively low compu-tational cost. Different member classifiers are trained based on different feature spaces respectively. Corre-sponding mass functions can be generated based on proposed mass function determination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.     

基于判别邻域嵌入的人脸识别

提出了一种人脸识别子空间方法:判别邻域嵌入(DNE).在框架中,训练样本数据的邻域和类关系被用来构建低维嵌入流形.在嵌入低维子空间后,同类样本保持它们固有的邻域关系,相反不同类近邻样本彼此远离.在ORL和Yale人脸数据库上,对提出的方法和主成分分析(PCA)、线性判别分析(LDA)、保持邻域嵌入(NPE)和保持局部投影(LPP)方法进行了比较,结果表明,提出的方法是有效的.     

一种核主元分析的人脸识别方法及其DSP实现

针对核主元分析在人脸识别中的时间开销过大的不足,提出了一种核主元分析的DSP实现方法.介绍了核主元分析算法原理,给出了DSP平台的硬件框架和程序优化方案.在DSP硬件平台上的测试结果表明:系统运行稳定,与通用计算机平台相比,明显提高了识别速度,同时保证了人脸识别率,具有一定的实用价值.     

基于随机非负独立元分析的掌纹识别

提出运用随机非负独立元分析（SN—ICA）的新方法进行掌纹识别。为了减少计算量,运用SN-ICA算法前,先采用主元分析（PCA）算法去除掌纹图像的二阶统计特征相关性,其余的高阶非负统计特征由SN-ICA分离。首先用PCA和SN-ICA提取投影向量,然后将训练图像和待识别图像向投影向量上投影得到低维特征向量,最后计算特征...     

基于PCA和ICA的人脸识别

提出利用主成分分析(PCA)和独立成分分析(ICA)相结合的方法对人脸进行识别。首先对预处理后的图像进行降维,即利用PCA算法对图像进行去二阶相关和降维处理,然后再利用ICA算法获得人脸影像独立基成分,利用人脸影像独立基来构造一子空间,最后利用待识别图像在这个空间上的投影系数进行人脸识别。从两个不同的数据集,将传统的PCA人脸识别算法和提出的人脸识别算法进行比较。从实验数据结果看,提出的PCA和ICA结合人脸识别算法优于传统的PCA人脸识别算法。     

Evaluation of image resolution and super-resolution on face recognition performance

While researchers strive to improve automatic face recognition performance, the relationship between image resolution and face recognition performance has not received much attention. This relationship is examined systematically and a framework is developed such that results from super-resolution techniques can be compared. Three super-resolution techniques are compared with the Eigenface and Elastic Bunch Graph Matching face recognition engines. Parameter ranges over which these techniques provide better recognition performance than interpolated images is determined.     

L0-norm sparse representation based on modified genetic algorithm for face recognition

The typical sparse representation for classification (SRC) exploits the training samples to represent the test samples, and classifies the test samples based on the representation results. SRC is essentially an L₀-norm minimization problem which can theoretically yield the sparsest representation and lead to the promising classification performance. We know that it is difficult to directly resolve L₀-norm minimization problem by applying usual optimization method. To effectively address this problem, we propose the L₀-norm based SRC by exploiting a modified genetic algorithm (GA), termed GASRC, in this paper. The basic idea of GASRC is that it modifies the traditional genetic algorithm and then uses the modified GA (MGA) to select a part of the training samples to represent a test sample. Compared with the conventional SRC based on L₁-norm optimization, GASRC can achieve better classification performance. Experiments on several popular real-world databases show the good classification effectiveness of our approach.     

分块CS-LBP和加权PCA的低分辨率人脸识别

针对局部二值模式(LBP)特征在低分辨率的人脸图 像上识别率较低的问题,提出了一种基于分块中心对称局部二值模式(CS-LBP,center symmetric local binary pattern)和加权主成分分析(PCA)算法的低分辨率人脸识别算法。 首先利用分块CS-LBP算子提取低分辨率人脸图像的特征;然后利用加权PCA算子对特 征进行降维, 从而得到更强的分类特征;最后利用最近邻分类器选出人脸最优分类类别并计算识别率。在 ORL人脸库上的实验表明,在人脸图像分辨率下降到(12×10)时,本 文算法的识别率仍能达 到85.00%,基本满足了实际运用中对识别率的要求,并且降低了运算 时间。