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1.
改进的人脸识别主分量分析算法 总被引：3，自引：0，他引：3
在应用于人脸识别领域的主分量分析(PCA)算法中,为了降低与外界光照变化相关的特征向量对提取特征的影响,提出了一种改进的主分量分析(MPCA)算法,利用相对应的标准方差对提取的特征矢量元素进行归一化处理.采用耶鲁大学的2个人脸数据库(Yale face database和Yaleface database B)进行了验证,实验结果表明,对于正面人脸和具有小角度姿态变化情况下的人脸,提出方法的性能优于传统的PCA和LDA(线性判别分析)算法,而运算量和PCA算法相同,大大低于LDA算法. 相似文献
2.
INTRODUCTION Automatic face recognition has become a very active research area in the last decade due to the new interest in, and need for, surveillance and security, telecommunication and digital libraries, hu-man-computer intelligent interaction, and smart en-vironments. The small sample size (SSS) problem is often encountered because the number of the samples is much smaller than the dimension of the sample space in face recognition. It results in the singularity of the within-class … 相似文献
3.
针对常见的基于PCA的人脸识别方法在识别过程中所遇到的计算量大、分类特征不佳等问题,提出了基于遗传算法的PCA+2DPCA的人脸识别方法,并通过实验,利用ORL人脸数据库验证了该方法的可行性。 相似文献
4.
介绍了AAM基本理论,提出基于AAM＋PCA＋SVM的人脸表情识别方法.首先获取人脸样本的纹理模型和形状模型,然后利用主成分分析（PCA）建立AAM模型,将该统计模型的AAM应用到人脸特征点定位,对人脸表情进行特征提取,将PCA训练用于识别的支持向量机过程中,并进行分类.实验表明该方法降低了算法的时间复杂度,定位准确率高,同时不影响人脸表情的识别率. 相似文献
5.
人脸识别是计算机视觉和模式识别领域的一个活跃课题,有着十分广泛的应用前景。给出了一种基于PCA和LDA方法的人脸识别系统的实现。首先该算法采用奇异值分解技术提取主成分,然后用F isher线性判别分析技术来提取最终特征,最后将测试图像的投影与每一训练图像的投影相比较,与测试图像最接近的训练图像被系统识别出,图像的比较采用了欧几里德距离,仿真结果表明了该方法的有效性。 相似文献
6.
7.
基于主分量分析的数字水印 总被引：7，自引：0，他引：7
王朔中 《上海大学学报(英文版)》2000,4(1):22-26
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8.
针对人脸识别中人脸图像的特征提取问题,提出了一种将全局特征与局部特征相融合的人脸识别方法.全局特征的提取采用主成分分析算法.主动外观模型定位58个特征点,在其中17个特征点处进行Gabor小波变换则可提取局部特征.归一化的全局匹配度(局部匹配度)可由测试图像和训练图像的全局特征(局部特征)得到.对归一化的全局匹配度和局部匹配度进行融合后,融合匹配度最大的训练图像所属的类即为识别结果.实验利用2个人脸图像数据库(AR和SJTU-IP-PR)测试该方法的识别率,结果表明该方法要优于PCA和EBGM,并且在一定的表情、光照和姿态变化的条件下是有效、稳健的. 相似文献
9.
基于核独立成分分析的静息态fMRI数据研究（英文） 总被引：1，自引：0，他引：1
为了方便提取静息态默认网络,降低功能核磁共振（fMRI）数据复杂度,克服独立成分分析只适合于源信号线性混合的限制,提出了特征降维和非线性变换的框架.首先采用主成分分析对fMRI信号的时间维度进行降维,将原始维度为153 594×128的fMRI数据降至153 594×5,以达到降低计算复杂度的目的,并保留95%的信息成分.然后利用基于高斯核的非线性独立成分分析即核独立成分分析来分析静息态fMRI数据并提取默认网络.实验结果表明,在分析静息态fMRI数据的过程中,核独立成分分析不仅能准确提取默认网络,而且降低了噪声,所得到的结果优于普通独立成分分析. 相似文献
10.
基于全寿命周期理论的工程项目健康检测体系构建 总被引：1，自引：0，他引：1
为了更有效地对工程项目的健康状态做出全面准确的评估,将全寿命期下工程项目所有监测指标按质量指标、费用指标、时间指标、各方面满意度和可持续发展指标等加以划分．并且基于该指标体系定性与定量指标相结合的特征,构建了PCA—PR分析模型．该模型先对全寿命期指标体系进行主成分分析（PCA）,从之前构建的全寿命期指标中甄选出一批可以作为工程项目健康检测分析的主要特征指标体系;再针对这些主要特征指标体系进行模式识别分析（PR）,即通过将工程项目即时可能状态划分为绿灯至红灯5种状态,利用模式识别模型和项目主要特征指标识别出项目任意时点的健康状态．最后结合实例进行相关分析,得出与实际情况较为吻合的分析结果,验证了该指标体系和模型的有效性． 相似文献
11.
Robust video foreground segmentation and face recognition 总被引：1，自引：0，他引：1
管业鹏 《上海大学学报(英文版)》2009,13(4):311-315
Face recognition provides a natural visual interface for human computer interaction (HCI) applications. The process of face
recognition, however, is inhibited by variations in the appearance of face images caused by changes in lighting, expression,
viewpoint, aging and introduction of occlusion. Although various algorithms have been presented for face recognition, face
recognition is still a very challenging topic. A novel approach of real time face recognition for HCI is proposed in the paper.
In view of the limits of the popular approaches to foreground segmentation, wavelet multi-scale transform based background
subtraction is developed to extract foreground objects. The optimal selection of the threshold is automatically determined,
which does not require any complex supervised training or manual experimental calibration. A robust real time face recognition
algorithm is presented, which combines the projection matrixes without iteration and kernel Fisher discriminant analysis (KFDA)
to overcome some difficulties existing in the real face recognition. Superior performance of the proposed algorithm is demonstrated
by comparing with other algorithms through experiments. The proposed algorithm can also be applied to the video image sequences
of natural HCI.
Project supported by the National Natural Science Foundation of China (Grant No.60872117), and the Leading Academic Discipline
Project of Shanghai Municipal Education Commission (Grant No.J50104) 相似文献
12.
为了在保证检测准确率的前提下提高检测效率,并优化SDN网络中基于流表特征的DDoS攻击检测算法,主要分析基于流表特征的DDoS攻击检测技术及其存在的不足,提出首先利用主成分分析优化流表特征,从中选出合适的特征子集,并采用支持向量机算法实现分类检测;然后搭建仿真网络环境,利用正常数据集与攻击数据集训练分类器进行测试实验;最后从检测准确率与检测时间两个维度对特征降维前后的检测方法进行对比。实验结果表明,经过特征降维的检测方法在不影响准确率的同时,有效提高了检测速率。 相似文献
13.
Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations 总被引：1，自引：0，他引：1
A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images , such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower (7%) error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority (1% in terms of recognition rate) to other polynomial kernel models. 相似文献
14.
A new customization approach based on support vector regression (SVR) is proposed to obtain individual headrelated
impulse response (HRIR) without complex measurement and special equipment. Principal component analysis (PCA) is
first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric
parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric
parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight
vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the
measured anthropometric parameters. Compared with a back-propagation neural network (BPNN) for nonlinear regression,
better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR
algorithm. 相似文献
impulse response (HRIR) without complex measurement and special equipment. Principal component analysis (PCA) is
first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric
parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric
parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight
vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the
measured anthropometric parameters. Compared with a back-propagation neural network (BPNN) for nonlinear regression,
better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR
algorithm. 相似文献
15.
YU Bin TIAN Feng-chun HE Qing-hu RAN Jian LV Bo HONG Xin LIU Tao BI Yu-tian 《重庆大学学报(英文版)》2016,15(4):127-138
In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds (sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform (WPT) is used to extract the original features of lung sounds; then the genetic algorithm (GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis (PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds. 相似文献
16.
Batch process monitoring based on multilevel ICA-PCA 总被引：1，自引：0，他引：1
In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis （MLICA-PCA）. Unlike the conventional multi-way principal component analysis （MPCA） method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels： the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted.I^2 T^2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method. 相似文献
17.
提出了一种用于设备性能退化评估的PCA-CMAC(主成分分析-小脑模型节点控制器)模型.该模型利用PCA进行特征提取,去除多个传感器信号特征的冗余信息,并且减少CMAC的输入维数;利用CMAC的局部泛化能力定量地评估设备的性能退化.给出了模型的实现过程,并将模型应用于钻削过程刀具状态的评估,试验结果证明该模型能基于刀具的正常状态,对刀具的磨损状态进行定量的评估.分析了CMAC中泛化参数g和量化参数r对评估结果的影响,g越大,CMAC的泛化能力越好,但各退化状态之间的区别越不明显;r越小,各退化状态之间越容易区分,但所需的权存储空间越大.2个参数的基本选择原则是CMAC的权存储空间应尽量小,与此同时,各退化状态之间应容易区分. 相似文献
18.
二维最大散度差鉴别准则和二维Fisher鉴别准则抽取的特征具有很强的相关性.本文在此基础上,通过对传统的基于向量的典型相关分析方法进行分析改进,提出了一种新的直接基于图像二维鉴别特征矩阵融合的二维典型相关分析方法,并将其应用于人脸识别的特征融合过程中.较基于向量的典型相关分析,该方法计算过程中构造的协方差矩阵维数大幅度减小.这在一定程度上避免了人脸识别中存在的"高维小样本问题",另一方面也使算法的速度明显提高. 相似文献
19.
为了提高身份识别系统的实时性、可靠性和便捷性,设计了基于DSP的人脸识别系统。基于肤色的人脸检测算法和基于小波变换的主成分分析的人脸识别算法构成了所设计系统的软件算法;DSP(DM642)微处理器、CCD传感器摄像头和显示屏等构成了该系统的硬件平台。测试结果表明,设计的人脸识别系统具有识别速度快和成功率高等优点。 相似文献
20.
Attribute reduction is necessary in decision making system. Selecting right attribute reduction method is more important. This paper studies the reduction effects of principal components analysis （PCA） and system reconstruction analysis , SRA） on coronary heart disease data. The data set contains 1723 records, and 71 attributes in each record. PCA and SRA are used to reduce attributes number （less than 71 ） in the data set. And then decision tree algorithms. C4.5, classification and regression tree （ CART）, and chi-square automatic interaction detector （ CHAID ）, are adopted to analyze the raw data and attribute reduced data. The parameters of decision tree algorithms, including internal node number, maximum tree depth, leaves number, and correction rate are analyzed. The result indicates that. PCA and SRA data can complete attribute reduction work. and the decision-making rate on the reduced data is quicker than that on the raw data： the reduction effect of PCA is better than that of SRA. while the attribute assertion of SRA is better than that of PCA. PCA and SRA methods exhibit good performance in selecting and reducing attributes. 相似文献