Description: Webcam Face Tracking,real-time images from a webcam and converts them to grayscale images. Then, it extracts pre-defined feature vectors from the images and sends them to Support Vector Machine (SVM) to get the classification. Using the result, our program will be able to control the mouse cursor in real-time. -Labs WebCam Face Tracking, real-time images from a webcam and converts the m to grayscale images. Then, it extracts pre-defined feature vectors from t he images and sends them to Support Vector Machi ne (SVM) to get the classification. Using the re sult. our program will be able to control the mouse cur prosecutors in real-time. Platform: |
Size: 301008 |
Author:po |
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Description: 支持向量机方法，用matlab实现，用于分类检测、模式识别，人脸检测等-Support Vector Machine method, the realization of Matlab for the detection and classification, pattern recognition, face detection Platform: |
Size: 2048 |
Author:韩乐 |
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Description: Webcam Face Tracking,real-time images from a webcam and converts them to grayscale images. Then, it extracts pre-defined feature vectors from the images and sends them to Support Vector Machine (SVM) to get the classification. Using the result, our program will be able to control the mouse cursor in real-time. -Labs WebCam Face Tracking, real-time images from a webcam and converts the m to grayscale images. Then, it extracts pre-defined feature vectors from t he images and sends them to Support Vector Machi ne (SVM) to get the classification. Using the re sult. our program will be able to control the mouse cur prosecutors in real-time. Platform: |
Size: 1693696 |
Author:po |
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Description: 基于模板匹配与支持矢量机的人脸检测
梁路宏 艾海舟 肖习攀 叶航军 徐光佑 张钹
。本文提出了一种将模板匹配与支持矢量机（SVM）相结合的人脸检测算法。算法首先使用双眼—人脸模板对进行粗筛选，然后使用SVM分类器进行分类。在模板匹配限定的子空间内采用“自举”方法收集“非人脸”样本训练SVM，有效地降低了训练的难度。实验结果的对比数据表明，该算法是十分有效的。-Based on template matching and support vector machine Face Detection Liang Wang Ai Haizhou Road, Xi Pan Xiao Ye Zhang Bo Xu Guangyou military aircraft. This paper proposes a template matching and support vector machine (SVM) a combination of face detection algorithm. Algorithm the first to use both eyes- face template to carry out coarse filter, and then use the SVM classifier to classify. Template matching in the sub-space limit the use of Platform: |
Size: 1135616 |
Author:cy |
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Description: 模糊支持向量机与主成分分析在人脸识别中的应用-Fuzzy Support Vector Machine with Principal Component Analysis Applied on Face Recognition Platform: |
Size: 5130240 |
Author:戴欢 |
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Description: 基于PSO训练SVM的人脸识别
利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径-PSO-based SVM for face recognition training using support vector machine learning ability in the performance of good performance, combined with KPCA feature extraction method, applied to face recognition, the method in experiments to identify the performance of a good performance for the field of face recognition has provided a new way to identify Platform: |
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Author:彭伟 |
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Description: Abstract
We present a component-based, trainable system for detecting
frontal and near-frontal views of faces in still gray
images. The system consists of a two-level hierarchy of Support
Vector Machine (SVM) classifiers. On the first level,
component classifiers independently detect components of
a face. On the second level, a single classifier checks if the
geometrical configuration of the detected components in the
image matches a geometrical model of a face. We propose
a method for automatically learning components by using
3-D head models. This approach has the advantage that
no manual interaction is required for choosing and extracting
components. Experiments show that the componentbased
system is significantly more robust against rotations
in depth than a comparable system trained on whole face
patterns. Platform: |
Size: 349184 |
Author:a |
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Description: 二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机
(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽
略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸
识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem of LDA algorithm can be overcome by two—dimensional LDA f 2DLDA)，and
Support Vector Machine(SVM)has the characteristic of structural risk minimization．In this paper，two methods were
combined and used for face recognition．Firstly，the original images were decomposed into high—frequency and low—frequency
components by Wavelet Transform(WT)．The high—frequency components were ignored，while the low—frequency components
can be obtained．Then．the liner discriminant features were extracted by 2DLDA，and”one VS rest”。strategy of SVMs for
muhiclass classification was chosen to perform face recognition． Experimental results based on ORL f Olivetti Research
Laboratory1 face database and Yale face database show the validity of 2DLDA+SVM algorithm for face recogn ition． Platform: |
Size: 236544 |
Author:费富里 |
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Description: 介绍了基于2DPCA和SVM支持向量机的方法来进行人脸识别-2DPCA and introduced based on support vector machine SVM method for face recognition Platform: |
Size: 996352 |
Author:taoye |
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Description: 关于SVM支持向量机的算法，对于研究手势识别和人脸识别有很大参考价值。-About SVM support vector machine algorithms, gesture recognition and face recognition for the study of great reference value. Platform: |
Size: 5613568 |
Author:知秋 |
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Description: 基于支持向量机的人脸检测训练集增强算法实现。根据支持向量机(support vector machine，简称SVM)~ ，对基于边界的分类算"~(geometric approach)~
言，类别边界附近的样本通常比其他样本包含有更多的分类信息．基于这一基本思路，以人脸检测问题为例．探讨了
对给定训练样本集进行边界增强的问题，并为此而提出了一种基于支持向量机和改进的非线性精简集算法
IRS(improved reduced set)的训练集边界样本增强算法，用以扩大-91l练集并改善其样本分布．其中，所谓IRS算法是指
在精简集(reduced se0算法的核函数中嵌入一种新的距离度量一一图像欧式距离一一来改善其迭代近似性能，IRS
可以有效地生成新的、位于类别边界附近的虚拟样本以增强给定训练集．为了验证算法的有效性，采用增强的样本
集训练基于AdaBoost的人脸检测器，并在MIT+CMU正面人脸测试库上进行了测试．实验结果表明通过这种方法
能够有效地提高最终分类器的人脸检测性能．-According to support vector machines(SVMs)，for those geometric approach based classification
methods，examples close to the class boundary usually are more informative than others．Taking face detection as an
example，this paper addresses the problem of enhancing given training set and presents a nonlinear method to tackle
the problem effectively．Based on SVM and improved reduced set algorithm (IRS)，the method generates new
examples lying close to the face／non—face class boundary to enlarge the original dataset and hence improve its
sample distribution．The new IRS algorithm has greatly improved the approximation performance of the original
reduced set(RS)method by embedding a new distance metric called image Euclidean distance(IMED)into the
keme1 function．To verify the generalization capability of the proposed method，the enhanced dataset is used to train
an AdaBoost．based face detector and test it on the MIT+CMU frontal face test set．The experimental results show
that the origina Platform: |
Size: 649216 |
Author:郭事业 |
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Description: 本文的目的是提供一个我开发的SSE优化的，C++库，用于人脸检测，你可以马上把它用于你的视频监控系统中。涉及的技术有：小波分析，尺度缩减模型（PCA,LDA,ICA），人工神经网络（ANN），支持向量机（SVM），SSE编程，图像处理，直方图均衡，图像滤波，C++编程，还有一下其它的人脸检测的背景知识-The purpose of this paper is to provide an I developed SSE optimized, C++ library, used for face detection, you can immediately use it for your video surveillance system. The technology involved are: wavelet analysis, scaling down model (PCA, LDA, ICA), artificial neural network (ANN), support vector machine (SVM), SSE programming, image processing, histogram equalization, image filtering, C++ programming, but also what other people have the background knowledge of face detection Platform: |
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Author:憨豆 |
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Description: 一篇很不错的关于人脸表情识别的论文。论文提出了一种基于人脸局部特征的表情识别方法，先选取人脸重要的局部特征,对得到的局部特征进行主成分分析,然后用支持向量机( SVM)设计局部特征分类器来确定测试表情图像中局部特征,同时设计支持向量机( SVM)表情分类器,确定表情图像的所属类别。-A very good facial expression recognition on paper. This paper proposes a feature based on local expression of face recognition, face first select the important local features, local features of the obtained principal component analysis, and support vector machine (SVM) classifier design to determine the local characteristics of test expression image local features, while design of support vector machine (SVM) classifier expression, determine the expression of the image category. Platform: |
Size: 428032 |
Author:王二 |
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Description: 先用gabor 小波滤波器,做特征提取，然后用支持向量机（SVM）做分类，来实现人脸检测.需要用matlab 2010 或更新的版本才能运行-the code is used for face detection.Firstly it use gabor wavelet filter for feature extraction,Secondly it use support vector machine (SVM）for classification.matlab 2010 required. Platform: |
Size: 163840 |
Author:xiang |
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Description: 进行人脸识别，提取男女性别特征，年龄特征，并且运用了支持向量机的方法进行识别-For face recognition, extraction gender characteristics, age characteristics, and using support vector machine (SVM) method to recognize
Platform: |
Size: 217088 |
Author:肖飞 |
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Description: 支持向量机及其在人脸识别中的应用研究
上海交通大学博士论文，在知网上面付费下载得到的。本文从应用的角度出发，较为全面地对一些相关问题进行探讨，并使用Visual C++实现了一个基于支持向量机的人脸识别软件—idTeller。 论文的主要工作和创新点包括： ·提出了两种基于VC边界的支持向量机参数选择算法—固定C算法和VC-CV算法。VC边界是两类支持向量机参数选择的一个理想准则，但它的一些固有缺点使其应用变得困难。本文通过将VC边界转化为VC指标，最终把问题归结为对最小包围体的求解，从理论上和计算上为VC边界的使用铺平了道路。在此基础之上，本文提出了两种基于VC边界的参数选择算法—固定C算法和VC-CV算法。在数个基准数据集上的实验表明，相比交叉验证算法，VC-CV算法不仅能获得性能更好的分类器，而且具有较低的计算复杂度。 ·使用序贯最小优化算法解决了最小包围体求解问题。最小包围体求解是计算VC指标的一个关键步骤，本文使用序贯最小优化算法对其求解，并对算法初始化、参数选择及更新等若干实现问题进行了深入地研究。在多个基准数据集上的实验表明，序贯最小优化算法能够快速而准确地解决最小包围体求解问题。-
Support vector machine and its application to face recognition
Shanghai Jiaotong University doctoral thesis, in HowNet above pay to get the download. From the application point of view, to more fully explore some related issues, and using Visual C-idTeller a support vector machine-based face recognition software. The main work and innovation of the paper include: two kinds of parameters of support vector machine based on the VC boundary selection algorithm- fixed-C algorithm and the VC-CV algorithm. VC boundaries are two types of support vector machine parameters to select the ideal criteria, but some of its inherent shortcomings make it difficult. This article by VC boundary for the VC index, and ultimately the problem is reduced to the solution of the minimum bounding volume, and paved the way for the use of the VC boundary from the theory and calculations. On this basis, we propose two parameter selection algorithm based on the VC boundary- fixed-C algorithm and the VC-CV algorit Platform: |
Size: 10384384 |
Author:Jessicaying |
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Search - FACE SUPPORT VECTOR MACHINE SVM