共查询到19条相似文献,搜索用时 78 毫秒
1.
传统的基于数据二阶统计矩的主元分析法(PCA)是一种有效的数据特征提取方法,是基于原始特征的一种线性变换。但是,当原始数据中存在非线性属性时,用主元分析法后留下的显著成分就可能不再反映这种非线性属性。而核主元分析则是基于原始数据的高阶统计量,是一种非线性变换,在图像识别中它可以描述多个像素之间的相关性。而KPCA方法只考虑了人脸图像的整体信息,没有考虑到局部特征信息。文章提出了分块核主元分析(MKPCA)的方法进行人脸识别,取得了很好的效果。 相似文献
2.
传统的PCA和LDA算法受限于“小样本问题”,且对像素的高阶相关性不敏感。论文将核函数方法与规范化LDA相结合,将原图像空间通过非线性映射变换到高维特征空间,并借助于“核技巧”在新的空间中应用鉴别分析方法。通过对ORL人脸库的大量实验表明,该方法在特征提取方面优于PCA,KPCA,LDA等其他方法,在简化分类器的同时,也可以获得高识别率。 相似文献
3.
PCA和KPCA都是基于欧氏距离提出的,这种距离对离群数据点比较敏感,而余弦角距离对离群数据更为鲁棒,在很多情况下具有更好的性能。充分利用余弦角距离的优势,提出两种新的特征抽取算法——基于余弦角距离的主成分分析(PCAC)和基于余弦角距离的核主成分分析(KPCAC)。在YALE人脸数据库与PolyU掌纹数据库上的实验表明,PCAC比PCA取得了更好的效果,KPCAC也表现出了很好的性能。 相似文献
4.
环境质量评价是一个多指标决策过程,考虑到评价指标众多关系复杂,该文运用降维效果显著、能有效解决非线性问题的核主成分分析(KPCA)方法对主成分分析(PCA)综合评价进行改进,建立环境质量综合评价模型。实证研究结果表明该模型能够较客观地反映不同地区的环境状况。 相似文献
5.
利用小波分解提取人脸特征技术和支持向量机 (SVM)分类模型 ,提出了一种基于个人身份认证的正面人脸识别算法 (或称为人脸认证方法 ) .针对 M个用户的人脸认证算法包括二个阶段 :(1)训练阶段 :使用小波分解方法对脸像训练集中的人脸图象进行特征提取 ,并用所提取的人脸特征向量训练 M个 SVM(对应 M个用户 ) ;(2 )认证阶段 :先由待认证者所声称的用户身份 (姓名或密码等 )确定对应的一训练好的 SVM,然后用这一 SVM对小波分解方法提取的待认证人的脸像特征向量进行分类 ,分类结果将显示待认证人所声称的身份是否真实 .利用 ORL人脸图象库对该算法的实验测试结果 ,以及与径向基函数神经网络作为分类器时的实验结果比较表明了该算法性能的优越性 相似文献
6.
针对BP等全局性神经网络收敛速度慢和局部极小的存在,用于人脸表情分类时,不仅实时性难以达到要求,而且识别精度也存在不确定性。为提高速度,加快收敛,提出一种基于局部性CMAC(Cerebellar Model Articulation Controller)神经网络的人脸表情识别方法。先对样本图像进行预处理,提取感兴趣的脸部区域,通过K-L(Karhunen-Loeve)变换对处理后的图像提取眼、嘴和鼻等重要特征点的位置和局部几何形状作为识别特征得到感兴趣的表情区域。最后将待测表情与标准表情的欧氏距离作为CMAC神经网络的输入,表情类型作为网络输出,对人脸7种典型表情进行识别。实验结果表明,基于CMAC的方法能有效地识别人脸表情,而且算法简单,学习速度快,可用于需要实时分析人脸表情的场合。 相似文献
7.
尽管基于Fisher准则的线性鉴别分析被公认为特征抽取的有效方法之一,并被成功地用于人脸识别,但是由于光照变化、人脸表情和姿势变化,实际上的人脸图像分布是十分复杂的,因此,抽取非线性鉴别特征显得十分必要。为了能利用非线性鉴别特征进行人脸识别,提出了一种基于核的子空间鉴别分析方法。该方法首先利用核函数技术将原始样本隐式地映射到高维(甚至无穷维)特征空间;然后在高维特征空间里,利用再生核理论来建立基于广义Fisher准则的两个等价模型;最后利用正交补空间方法求得最优鉴别矢量来进行人脸识别。在ORL和NUST603两个人脸数据库上,对该方法进行了鉴别性能实验,得到了识别率分别为94%和99.58%的实验结果,这表明该方法与核组合方法的识别结果相当,且明显优于KPCA和Kernel fisherfaces方法的识别结果。 相似文献
8.
一种新的核线性鉴别分析算法及其在人脸识别上的应用 总被引:1,自引:0,他引:1
基于核策略的核Fisher鉴别分析(KFD)算法已成为非线性特征抽取的最有效方法之一。但是先前的基于核Fisher鉴别分析算法的特征抽取过程都是基于2值分类问题而言的。如何从重叠(离群)样本中抽取有效的分类特征没有得到有效的解决。本文在结合模糊集理论的基础上,利用模糊隶属度函数的概念,在特征提取过程中融入了样本的分布信息,提出了一种新的核Fisher鉴别分析方法——模糊核鉴别分析算法。在ORL人脸数据库上的实验结果验证了该算法的有效性。 相似文献
9.
10.
传统的PCA和LDA算法受限于“小样本问题”,且对象素的高阶相关性不敏感。本文将核函数方法与规范化LDA相结合,将原图像空间通过非线性映射变换到高维特征空间,并借助于“核技巧”在新的空间中应用鉴别分析方法。通过对ORL人脸库的大量实验研究表明,本文方法在特征提取方面明显优于PCA,KPCA,LDA等其他传统的人脸识别方法,在简化分类器的同时,也可以获得高识别率。 相似文献
11.
Guang Dai Author Vitae Author Vitae Yun-Tao Qian Author Vitae 《Pattern recognition》2007,40(1):229-243
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kernel fractional-step discriminant analysis (KFDA) for nonlinear feature extraction and dimensionality reduction. Not only can this new algorithm, like other kernel methods, deal with nonlinearity required for many face recognition tasks, it can also outperform traditional KDA algorithms in resisting the adverse effects due to outlier classes. Moreover, to further strengthen the overall performance of KDA algorithms for face recognition, we propose two new kernel functions: cosine fractional-power polynomial kernel and non-normal Gaussian RBF kernel. We perform extensive comparative studies based on the YaleB and FERET face databases. Experimental results show that our KFDA algorithm outperforms traditional kernel principal component analysis (KPCA) and KDA algorithms. Moreover, further improvement can be obtained when the two new kernel functions are used. 相似文献
12.
13.
研究了基于Gabor特征量和核函数判决方法的人脸识别方法,即首先利用Gabor滤波器组对输入样本进行处理,获得Gabor特征量;然后利用核函数判决方法实现人脸识别。Gabor滤波器组通过提取具有空间频率、空间位置和取向选择性的特征,较好克服了实际中由于表情和光照不同带来的变化;而核函数判决分析方法具有提取输入样本空间的非线性最佳鉴别特征的优点。实验仿真表明了该方法的有效性。 相似文献
14.
Qingshan Liu Xiaoou Tang Hanqing Lu Songde Ma 《Neural Networks, IEEE Transactions on》2006,17(4):1081-1085
There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analysis is proposed to overcome these two problems. We first use the nonlinear kernel trick to map the input data into an implicit feature space F. Then a scatter-difference-based discriminant rule is defined to analyze the data in F. The proposed method can not only produce nonlinear discriminant features but also avoid the singularity problem of the within-class scatter matrix. Extensive experiments show encouraging recognition performance of the new algorithm. 相似文献
15.
Face recognition using kernel direct discriminant analysis algorithms 总被引:22,自引:0,他引:22
Juwei Lu Plataniotis K.N. Venetsanopoulos A.N. 《Neural Networks, IEEE Transactions on》2003,14(1):117-126
Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. It is, therefore, not surprising that linear techniques, such as those based on principle component analysis (PCA) or linear discriminant analysis (LDA), cannot provide reliable and robust solutions to those FR problems with complex face variations. In this paper, we propose a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution. The proposed method also effectively solves the so-called "small sample size" (SSS) problem, which exists in most FR tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34% and 48% of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the generalized discriminant analysis (GDA), respectively. 相似文献
16.
Face recognition based on a novel linear discriminant criterion 总被引:1,自引:0,他引:1
Fengxi Song David Zhang Qinglong Chen Jizhong Wang 《Pattern Analysis & Applications》2007,10(3):165-174
As an effective technique for feature extraction and pattern classification Fisher linear discriminant (FLD) has been successfully applied in many fields. However, for a task with very high-dimensional data such as face images,
conventional FLD technique encounters a fundamental difficulty caused by singular within-class scatter matrix. To avoid the
trouble, many improvements on the feature extraction aspect of FLD have been proposed. In contrast, studies on the pattern
classification aspect of FLD are quiet few. In this paper, we will focus our attention on the possible improvement on the
pattern classification aspect of FLD by presenting a novel linear discriminant criterion called maximum scatter difference (MSD). Theoretical analysis demonstrates that MSD criterion is a generalization of Fisher discriminant criterion, and is
the asymptotic form of discriminant criterion: large margin linear projection. The performance of MSD classifier is tested in face recognition. Experiments performed on the ORL, Yale, FERET and AR databases
show that MSD classifier can compete with top-performance linear classifiers such as linear support vector machines, and is better than or equivalent to combinations of well known facial feature extraction methods, such as eigenfaces, Fisherfaces, orthogonal complementary space, nullspace, direct linear discriminant analysis, and the nearest neighbor classifier.
相似文献
Fengxi SongEmail: |
17.
针对人脸识别中的非线性特征提取和有标记样本不足问题,提出了在核空间具有正交性半监督鉴别矢量的计算方法。算法利用核函数将人脸数据映射到高维非线性空间,在该空间采用边界Fisher判别分析(Marginal Fisher Analysis,MFA)算法将少量有类别标签样本进行降维,同时采用无监督鉴别投影(Unsupervised Discriminant Projection,UDP)对大量无标签样本进行学习,以半监督的方法构造算法的目标函数,在特征值求解时以正交方式找出最优投影向量,进行人脸识别。通过实验,在ORL和YALE人脸数据库上验证了该算法的有效性。 相似文献
18.
19.
Jose Portillo-Portillo Roberto Leyva Victor Sanchez Gabriel Sanchez-Perez Hector Perez-Meana Jesus Olivares-Mercado Karina Toscano-Medina Mariko Nakano-Miyatake 《Applied Intelligence》2018,48(5):1200-1217
This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy. 相似文献