共查询到20条相似文献,搜索用时 31 毫秒
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Linear subspace analysis methods have been successfully applied to extract features for face recognition.But they are inadequate to represent the complex and nonlinear variations of real face images,such as illumination,facial expression and pose variations,because of their linear properties.In this paper,a nonlinear subspace analysis method,Kernel-based Nonlinear Discriminant Analysis (KNDA),is presented for face recognition,which combines the nonlinear kernel trick with the linear subspace analysis method-Fisher Linear Discriminant Analysis (FLDA).First,the kernel trick is used to project the input data into an implicit feature space,then FLDA is performed in this feature space.Thus nonlinear discriminant features of the input data are yielded.In addition,in order to reduce the computational complexity,a geometry-based feature vectors selection scheme is adopted.Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA),which combines the kernel trick with linear Principal Component Analysis (PCA).Experiments are performed with the polynomial kernel,and KNDA is compared with KPCA and FLDA.Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA. 相似文献
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Decision-level fusion for single-view gait recognition with various carrying and clothing conditions
Gait recognition is one of the latest and attractive biometric techniques, due to its potential in identification of individuals at a distance, unobtrusively and even using low resolution images. In this paper we focus on single lateral view gait recognition with various carrying and clothing conditions. Such a system is needed in access control applications whereby a single view is imposed by the system setup. The gait data is firstly processed using three gait representation methods as the features sources; Accumulated Prediction Image (API) and two new gait representations namely; Accumulated Flow Image (AFI) and Edge-Masked Active Energy Image (EMAEI). Secondly, each of these methods is tested using three matching classification schemes; image projection with Linear Discriminant Functions (LDF), Multilinear Principal Component Analysis (MPCA) with K-Nearest Neighbor (KNN) classifier and the third method: MPCA plus Linear Discriminant Analysis (MPCA + LDA) with KNN classifier. Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. This arrangement results into nine recognition sub-systems. Decisions from the nine classifiers are fused using decision-level (majority voting) scheme. A comparison between unweighted and weighted voting schemes is also presented. The methods are evaluated on CASIA B Dataset using four different experimental setups, and on OU-ISIR Dataset B using two different setups. The experimental results show that the classification accuracy of the proposed methods is encouraging and outperforms several state-of-the-art gait recognition approaches reported in the literature. 相似文献
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针对主成分分析(PCA)算法在人脸识别中识别率低的问题,提出一种图像纹理频谱特征与PCA相结合的人脸识别算法。该算法利用纹理单元算子提取人脸图像纹理频谱特征,然后用PCA对所提取的特征降维,最后利用最近邻(KNN)分类器进行人脸识别。在ORL人脸库和Yale人脸库上对所提出的算法进行了测试,识别率均高于PCA、模块化二维PCA(M2DPCA)等方法,分别为96.5%和95%。实验结果表明了该算法的有效性和准确性。 相似文献
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Tae-Kyun Kim Author Vitae Hyunwoo Kim Author Vitae Author Vitae Josef Kittler Author Vitae 《Pattern recognition》2004,37(9):1873-1885
In this paper, we propose an Independent Component Analysis (ICA) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most Principal Component Analysis (PCA) based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the “residual face space”. We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations. 相似文献
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一种融合KPCA和KDA的人脸识别新方法 总被引:2,自引:0,他引:2
核判别分析(KDA)和核主成分分析(KPCA)分别是线性判别分析(LDA)和主成分分析(PCA)在核空间中的非线性推广,提出了一种融合KDA和KPCA的特征提取方法并应用于人脸识别中,该方法综合利用KDA和KPCA 的优点来提高人脸识别的性能。此外,还提出了一种广义最近特征线(GNFL)方法来构造有效的分类器。实验结果证明:提出的方法获得了更好的识别结果。 相似文献
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Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two popular feature extraction techniques in statistical pattern recognition field. Due to small sample size problem LDA cannot be directly applied to appearance-based face recognition tasks. As a consequence, a lot of LDA-based facial feature extraction techniques are proposed to deal with the problem one after the other. Nullspace Method is one of the most effective methods among them. The Nullspace Method tries to find a set of discriminant vectors which maximize the between-class scatter in the null space of the within-class scatter matrix. The calculation of its discriminant vectors will involve performing singular value decomposition on a high-dimensional matrix. It is generally memory- and time-consuming. Borrowing the key idea in Nullspace method and the concept of coefficient of variance in statistical analysis we present a novel facial feature extraction method, i.e., Discriminant based on Coefficient of Variance (DCV) in this paper. Experimental results performed on the FERET and AR face image databases demonstrate that DCV is a promising technique in comparison with Eigenfaces, Nullspace Method, and other state-of-the-art facial feature extraction methods. 相似文献
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传统的基于统计的子空间学习算法如主成分分析,通过学习只能得到一系列特征脸,忽略了人脸识别中重要的局部信息(如眼睛、鼻子)。而利用到类别信息的算法如线性判别分析,也会因为小样本问题而有所影响。为了解决这些问题,结合二维偏最小二乘与非负矩阵分解的非负性思想提出二维非负偏最小二乘(Two-Dimensional Nonnegative Partial Least Squares,2DNPLS)算法。其核心思想是在提取人脸特征时加入了非负性约束,使得2DNPLS不仅拥有偏最小二乘算法加入类别信息带来的分类效果,还保留了图像矩阵的内部结构信息,而且还使得到的基矩阵具有非负的局部的可解释性。在ORL,Yale人脸库中的实验结果表明,该算法从时间上和识别率上均优于人脸识别的主流算法。 相似文献
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针对图嵌入方法在构造邻域关系图的过程中,简单地将样本数据划入某一类的做法并不妥当的问题,提出了模糊渐进的隶属度表示方法。该方法借助模糊数学的思想,通过模糊渐进的隶属度,将样本归属于不同类别。针对图嵌入方法中分类器效率偏低的问题,引入了协作表示分类方法,该分类方法大幅度提高了算法的计算效率。基于这两点,提出了基于协作表示和模糊渐进最大边界嵌入的特征抽取算法。在ORL、AR人脸数据库上,以及USPS数字手写体数据库上的实验表明,该算法优于主成分分析(PCA)、线性鉴别分析(LDA)、局部保留投影(LPP)和边界Fisher分析(MFA)。 相似文献
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为了评估亚健康状态,提出一种基于心电信号小波包变换和主成分分析的亚健康状态识别新方法。采用小波包变换对心电信号进行特征提取;再利用主成分分析(PCA)对所提特征进行降维处理,以剔除特征之间的冗余信息;最后应用线性判别式分析(LDA)对亚健康状态进行分类识别。研究结果显示,该方法能获得较高的识别率,对于实现亚健康状态的评估具有一定的参考价值。 相似文献
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George D.C. Cavalcanti Tsang Ing Ren José Francisco Pereira 《Expert systems with applications》2013,40(12):4971-4977
This paper proposes two feature extraction techniques that minimizes the effects of distortions generated by variations in illumination, rotation and, head pose in automatic face recognition systems. The proposed techniques are Modular IMage Principal Component Analysis (MIMPCA) and weighted Modular Image Principal Component Analysis (wMIMPCA). Both techniques are based on PCA and they use the modular image decomposition to minimize local variation. Also, the covariance matrix is calculated directly from the original image matrix. This strategy generates a smaller matrix compared with traditional PCA and reduces the computational effort. wMIMPCA assumes that parts of the face are more discriminatory than others, so a Genetic Algorithm is used to obtain weights for each region in the face image. The proposed techniques are compared with Modular PCA and two-dimensional PCA using three well-known databases, showing better results. 相似文献
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In this paper, an efficient facial expression recognition system using ANFIS-MHS
(Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking)
has been proposed. The features were extracted using MLDA (Modified Linear
Discriminant Analysis) and then the optimized parameters are computed by
using mGSO (modified Glow-worm Swarm Optimization).The proposed system
recognizes the facial expressions using ANFIS-MHS. The experimental results
demonstrate that the proposed technique is performed better than existing
classification schemes like HAKELM (Hybridization of Adaptive Kernel based
Extreme Learning Machine), Support Vector Machine (SVM) and Principal
Component Analysis (PCA). The proposed approach is implemented in MATLAB. 相似文献
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为了提高人脸识别效率,提出了一种基于PCA、LDA和SVM算法融合的人脸识别方法。使用主成分分析(PCA)将人脸图像变换到新的特征空间中,消除图像特征间的相关性和噪声,提取人脸全局特征,在实验阶段取较多的投影方向使其尽可能多的保持原始信息;使用线性判别分析(LDA)算法进一步投影变换降低数据维度;使用支持向量机(SVM)分类识别。将PCA、LDA和SVM三种算法的优点结合起来,在ORL数据库上进行仿真实验,结果表明该方法的识别率可达99.0%。 相似文献
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针对人脸图像易受光线和表情影响的特点,提出了一种基于二进小波变换和仿生模式识别的人脸识别方法。应用样条二进小波对人脸图像进行处理,对得到的细节子图进行融合。在FFT和PCA处理与降维后,用仿生模式识别进行学习和识别。实验结果表明,该方法比传统方法具有更高的识别率。 相似文献
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Independent shape component-based human activity recognition via Hidden Markov Model 总被引:3,自引:2,他引:1
In proactive computing, human activity recognition from image sequences is an active research area. In this paper, a novel
human activity recognition method is proposed, which utilizes Independent Component Analysis (ICA) for activity shape information
extraction from image sequences and Hidden Markov Model (HMM) for recognition. Various human activities are represented by
shape feature vectors from the sequence of activity shape images via ICA. Based on these features, each HMM is trained and
activity recognition is achieved by the trained HMMs of different activities. Our recognition performance has been compared
to the conventional method where Principal Component Analysis (PCA) is typically used to derive activity shape features. Our
results show that superior recognition is achieved with the proposed method especially for activities (e.g., skipping) that
cannot be easily recognized by the conventional method. Furthermore, by employing Linear Discriminant Analysis (LDA) on IC
features, the recognition results further improved significantly in the recognition performance. 相似文献
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在iOS平台上开发了一款人脸识别系统,借助OpenCV函数库实现了基于Haar-like特征和AdaBoost算法的人脸检测。提出了主成分分析和线性判别分析相结合的人脸识别算法,既避免了主成分分析方法对图像信息不分主次、忽视类别信息的缺陷,又降低了线性判别分析算法高运算量导致的大误差、小样本的局限性。实验结果表明该系统的识别效果良好。 相似文献
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结合模糊集理论、双向二维主成分-线性鉴别分析((2D)2PCALDA)的特点,提出一种新的人脸图像特征提取方法。算法首先对人脸图像进行二维主成分分析(2DPCA)处理,再用模糊K近邻算法计算图像的隶属度矩阵,并将其融入到2DLDA过程中,从而得到模糊类间散射矩阵和模糊类内散射矩阵。与(2D2PCALDA相比,该算法充分利用了(2D)2PCALDA的优点,有效地提取了行和列的识别信息,并充分考虑了样本的分布信息。在Yale和FERET人脸数据库上的实验结果表明,该方法识别效果优于(2D)2PCALDA、双向二维主成分分析((2D)2PCA)等方法。 相似文献
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基于人体轮廓宽度特征的步态识别 总被引:3,自引:0,他引:3
基于人体轮廓宽度特征提出了一种步态识别算法。首先对每个序列进行运动轮廓抽取,将这些时变的二维轮廓形状转换为对应的一维横向宽度信号,通过主元分析法(PCA)来提取低维步态特征,在此基础上采用线性判决分析(LDA),以获取最佳投影方向,达到提高数据分类能力的目的。在NLPR、CMU和UMF步态数据库中进行实验,结果表明算法具备快速、稳健特征,在实际应用中具备较大的价值。 相似文献