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。     

基于Gabor和PCA的面部图像识别方法

文中利用Gabor变换和PCA降维的优点,提出了Gabor+PCA的面部图像识别方法.该方法先提取图像的Gabor特征,然后将Gabor特征与原图像特征结合构成新的融合特征并用PCA降维,最后用KNN分类器分类.所提Gabor+PCA方法不仅能挖掘出图像的细节信息,而且拓宽了特征空间的维数.另外,Gabor+PCA方法...     

基于Gabor小波和支持向量机的人脸识别方法

基于Gabor小波在图像表征方面的优越性,提出了将Gabor小波和支持向量机(SVM)相结合用于人脸识别的方案。用Gabor小波对人脸图像进行特征提取,再利用SVM策略对特征向量进行分类识别,实验的仿真结果验证了本算法的有效性。     

基于Gabor小波的人脸表情特征提取研究

为了使计算机能更好的识别人脸表情,对基于Gabor小波变换的人脸表情识别方法进行了研究。首先对包含表情区域的静态灰度图像进行预处理,包括对确定的人脸表情区域进行尺寸和灰度归一化,然后利用二维Gabor小波变换提取脸部表情特征,使用快速PCA方法对提取的Gabor小波特征初步降维。再在低维的空间中,利用Fisher准则提取那些有利于分类的特征,最后用SVM分类器进行分类。实验结果表明,上述提出的方法比传统的方法识别速度更快,能达到实时性的要求,并且具有很好的鲁棒性,识别率高。     

基于最大Gabor相似度的多光谱人脸识别系统

以精准识别不同特征维数以及噪声情况下的多光谱人脸为目标,设计基于最大Gabor相似度的多光谱人脸识别系统.原始可见光光谱图像以及热红外光谱图像分别输入系统的可见光光谱图像处理模块以及热红外光谱图像处理模块,图像处理模块分别提取原始可见光光谱图像以及热红外光谱图像特征并建立特征集,多光谱图像融合模块基于特征集内特征利用双...     

Active appearance models using statistical characteristics of Gabor based texture representation

Active appearance model (AAM) has been successfully applied to register many types of deformable objects in images. However, the high dimension of intensity used in AAM usually leads to an expensive storage and computational cost. Moreover, intensity values cannot provide enough information for image alignment. In this paper, we propose a new AAM method based on Gabor texture feature representation. Our contributions are two-fold. On one hand, based on the assumption that Gabor magnitude and Gabor phase follow a lognormal distribution and a general Gaussian distribution respectively, three simplified texture representations are proposed. One the other hand, we apply the proposed texture representations in AAM, which is the first time to extract statistical features from both Gabor magnitude and Gabor phase as the texture representation in AAM. Tests on public and our databases show that the proposed Gabor representations lead to more accurate and robust matching between model and images.     

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.     

人脸识别中视频回放假冒攻击的实时检测方法

利用合法用户的脸部视频进行回放假冒攻击,是目前人脸认证系统的重要安全威胁。针对此问题,本文提出了一种仅用单个普通摄像头来抵抗人脸视频假冒攻击的方法。不同于以往从人脸区域中获取假冒线索进行活体检测的方法,本文通过人脸输入图像与场景参考图像之间的背景对比,从人脸周围背景区域中寻求视频假冒攻击线索。首先,本文在尺度空间里构建人脸周围区域图像的背景特征点集合;然后,利用背景特征点集合建立识别场地背景和人脸背景的Gabor背景描述子,并用融合相位补差的相似度来进行背景比对。实验表明该方法能有效地识别视频回放假冒攻击。     

基于Gabor小波与Memetic算法的人脸识别方法

提出一种基于Gabor小波与Memetic算法的人脸识别方法MA-Gabor(Memetic Algorithm-Gabor).算法使用一组特定的Gabor小波滤波器对人脸图像重要区域进行针对性的特征提取运算,可在较短处理时间内获得更具区分能力的识别数据.为提升识别性能,MA-Gabor引入Memetic算法用于Gabor小波滤波器组的优化设计.实验结果表明,Memetic算法可获得比传统优化方法更佳的设计效果.通过将优化设计的Gabor小波滤波器组用于人脸图像的特征提取,MA-Gabor算法可取得比现有人脸识别方法更高的识别率.     

Asymboost-based fisher linear classifier for face recognition

When using AdaBoost to select discriminant features from some feature space （e.g. Gabor feature space） for face recognition, cascade structure is usually adopted to leverage the asymmetry in the distribution of positive and negative samples. Each node in the cascade structure is a classifier trained by AdaBoost with an asymmetric learning goal of high recognition rate but only moderate low false positive rate. One limitation of AdaBoost arises in the context of skewed example distribution and cascade classifiers： AdaBoost minimizes the classification error, which is not guaranteed to achieve the asymmetric node learning goal. In this paper, we propose to use the asymmetric AdaBoost （Asym-Boost） as a mechanism to address the asymmetric node learning goal. Moreover, the two parts of the selecting features and forming ensemble classifiers are decoupled, both of which occur simultaneously in AsymBoost and AdaBoost. Fisher Linear Discriminant Analysis （FLDA） is used on the selected features to learn a linear discriminant function that maximizes the separability of data among the different classes, which we think can improve the recognition performance. The proposed algorithm is demonstrated with face recognition using a Gabor based representation on the FERET database. Experimental results show that the proposed algorithm yields better recognition performance than AdaBoost itself.     

基于Gabor小波和主元分析的纹理图像分割

提出了基于Gabor小波和主元分析相结合的纹理图像分割算法.首先对纹理图像进行多通道滤波,获得了一系列滤波后的纹理图像.其次,借助于“能量测度”的概念,求解出各象素有效的纹理特征.为了进一步减少特征之间的信息冗余,降低聚类分析的计算负荷,采用主元分析（PCA）对所得的纹理特征进行降维.然后利用K-Mean算法实现纹理图像的分类.最后针对所提算法,进行了仿真试验.     

基于局部二值模式的医学图像检索

提出了一种基于局部二值模式(LBP)和纹理模式统计进行医学图像检索的方法,计算了LBP和局部方差的联合直方图,改进了Log-likelihood统计距离度量算法.通过仿真表明:改进的Log-likelihood统计算法比Log-likelihood统计算法检索准确率高:与基于Gabor纹理特征图像检索相比较,该局部二值纹理模式检索算法检索准确率能提高8%以上.     

一种基于Gabor变换的HRRP目标识别方法

从模式分类角度讨论了基于高分辨距离像的舰船目标识别问题,提出了一种基于Gabor变换的HRRP目标识别方法。通过对数据进行Gabor变换去除噪声,利用基于小波包分解能量的方法得到信号在不同频带的能量分布特性,最终通过支持向量机进行识别。通过对6类目标实测数据的分析,验证结果表明,该方法在舰船目标识别领域具有良好的应用前景。     

Locality-sensitive kernel sparse representation classification for face recognition

In this paper a new classification method called locality-sensitive kernel sparse representation classification (LS-KSRC) is proposed for face recognition. LS-KSRC integrates both sparsity and data locality in the kernel feature space rather than in the original feature space. LS-KSRC can learn more discriminating sparse representation coefficients for face recognition. The closed form solution of the l₁-norm minimization problem for LS-KSRC is also presented. LS-KSRC is compared with kernel sparse representation classification (KSRC), sparse representation classification (SRC), locality-constrained linear coding (LLC), support vector machines (SVM), the nearest neighbor (NN), and the nearest subspace (NS). Experimental results on three benchmarking face databases, i.e., the ORL database, the Extended Yale B database, and the CMU PIE database, demonstrate the promising performance of the proposed method for face recognition, outperforming the other used methods.     

应用Gabor小波和支持向量机的纹理分类

针对现有纹理分类算法的局限性,提出了一种基于Gabor小波和支持向量机的纹理分类算法.首先提取纹理Gabor分解后各子带的均值和方差作为特征向量,进而利用支持向量机算法实现分类.实验结果表明,与传统的分类方法相比,Gabor小波和支持向量机相结合能有效地提高分类正确率.     

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.