Description: 基于小波变换和神经网络的人脸识别：本论文围绕人脸识别问题对人脸特征提取及识别技术进行了研究。主要有：对人脸识别的研究工作进行了综述；在KL算法的基础上提出了新的基于KL的特征提取方法，克服了KL算法计算量大，计算时间长的缺点，-Based on Wavelet Transform and Neural Network Face Recognition: In this paper, issues surrounding the face recognition feature extraction and face recognition technology is studied. Mainly include: the research work on face recognition are reviewed in the KL algorithm is proposed based on the new KL-based feature extraction methods, the KL algorithm to overcome the large amount of computing time of shortcomings, Platform: |
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Author:hyh |
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Description: Face Recognition Library
=== === === ===
Advanced face recognition DLL using two functions : Train and Recognize. Uses neural net back propogation alogorithm with more AI tools added for imaging optimization. Library works great even for a low resolution web cam image and requires the user to align to a mirror frame on screen. Complete Source Code with Video capture and feature extraction kit for Registered Users.
Please register here for only $299 with Source Code :
http://www.research-lab.com/facerecognitionorder.htm
(c) www.research-lab.com-Face Recognition Library
========================
Advanced face recognition DLL using two functions : Train and Recognize. Uses neural net back propogation alogorithm with more AI tools added for imaging optimization. Library works great even for a low resolution web cam image and requires the user to align to a mirror frame on screen. Complete Source Code with Video capture and feature extraction kit for Registered Users.
Please register here for only $299 with Source Code :
http://www.research-lab.com/facerecognitionorder.htm
(c) www.research-lab.com Platform: |
Size: 892928 |
Author:ant |
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Description: 提出一种新的非线性变换的彩色空间 ″″, 利用次高斯概率分布函数拟合皮肤色度信息, 得到候选区
YC C
r b
域。为了排除候选区域中的非人脸, 首先根据均值和方差信息分割出候选区域中的纹理特征信息, 再通过多尺度
)
(
信息定位眼睛, 然后根据人脸特征的几
形态边缘检测算子检测候选区域的边缘, 利用 边缘方向
PCA PCAED
( )
何形状信息定位其他特征 鼻、嘴 , 通过这些几何特征信息对肤色分割得到的候选区域进行验证, 最终得到正确
的人脸区域。利用3 个实验数据集测试该算法, 并与其它相应的算法相比较, 提出的非线性彩色空间对于肤色分
割具有很好的效果, 且对光照和姿态具有良好的不变性。另外, 利用 信息和几何特征信息检测人脸特征
PCAED
具有很高的定位精度, 定位检测率优于其他方法。实验结果表明, 该算法具有定位准确率高, 漏检率和误检率低
等特点。-
A novel approach for skin segmentation and facial feature extraction is proposed
The proposed skin segmentation is a method for integrating the chrominance components of
″″ . ″″
r b r b
nonlinear YC C color model The chrominance components of nonlinear YC C color space
,
are modeled using a subgaussian probability density function and then the face skin is seg
. ,
mented based on this function In order to authenticate the face candidate regions firstly tex
ture information in face candidate regions is segmented using mean and variance of luminance
, . ,
information and then the eye is located by the PCA edge direction information Finally the
, ,
others features such as nose and mouth also are detected using the geometrical shape infor
. 2 ,
mation As all the above mentioned techniques are simple and efficient the skin segmentation
.
based on nonlinear color spacemethod has the invariability of lighting and pose In the experi
, .
ments themethod has been successfull Platform: |
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Author:zz |
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Description: 该软件最主要的功能就是要能识别出人脸，首先该系统需要对通过摄像头拍照而获取到的原始的人脸图片进行一系列处理才可进行下一步的工作，该处理过程也称图像预处理。预处理这个模块在整个人脸识别系统的开发过程中占有很重要的地位，只有预处理模块做的好，才可能很好的完成后面的人脸定位和特征提取这两大关键模块。-The main features of the software is to be able to identify the face, first of all, the system need to take pictures through the camera and access to the original face image can only be carried out a series dealing with the work of the next step, the process also known as image pretreatment. Pretreatment of the Face Recognition System module in the entire process of development plays an important role, and only do pre-processing module, and can be very good after the completion of the Human Face Localization and Feature Extraction of these two key modules. Platform: |
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Author:chiaks |
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Description: 基于非负矩阵分解（NMF）的人脸特征提取算法，NMF基本思想是找到一个线性子空间W，使的构成子空间的基本图像的像素点都是正值，而且人脸图像在子空间上的投影系数也是正数-Non-negative Matrix Factorization (NMF) of facial feature extraction algorithm, NMF basic idea is to find a linear sub-space W, so that the composition of sub-space of the basic image pixels are positive, and face image in the sub-space projection coefficient is positive Platform: |
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Author:xuying |
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Description: 人脸定位实例！能够准确定位人脸的位置并能提取人脸特征！-Examples of Face Location! Able to accurately position the location of face and facial feature extraction! Platform: |
Size: 65536 |
Author:andy |
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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: （压缩包里一共有5个代码）
pca+lda+粗糙集+模糊神经网络
saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat
1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat
2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat
3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat
4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat
5.用runfnn.m对rstest进行测试得到其识别率
savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.-pca+ lda+ Rough Set+ fuzzy neural network
saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat
Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat
2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat
3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat
4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat
5. Rstest used to test for runfnn.m by its recognition rate
cm.m is savem.m and minimum distance classifier on the training set and test set classificati Platform: |
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Author:dong |
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Description: 人脸识别是生物特征识别技术中一个非常活跃的课题,取得了很多研究成果。统计主元分析法( Prin2cipal ComponentsAnalysis, PCA)是人脸特征提取和识别的常用方法之一。-Face recognition is an active subject in the area of biometrical recognition technology, and lots of achievements have
been obtained. Principal ComponentsAnalysis ( PCA) is a basicmethod widely used in face feature extraction and recognition.
In this paper, combined with the characteristics of traditional PCA, Platform: |
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Author:junjun |
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Description: This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method.-This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This is based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method. Platform: |
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Author:Swati |
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Description: 关于人脸特征提取的最新论文，是研究人脸识别的重要参考资料！-Face feature extraction on the latest paper is to study the face recognition of the important references! Platform: |
Size: 5193728 |
Author:suiqing |
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Description: 关于人脸特征提取的最新论文，是研究人脸识别的重要参考资料！-Face feature extraction on the latest paper is to study the face recognition of the important references! Platform: |
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Author:suiqing |
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Description: 视频搜索中人脸识别关键技术的研究与实现。本文对人脸检测与识别技术进行了研究，实现了一个用于视频搜
索的自动人脸识别系统。该系统对输入的视频帧进行人脸检测和定
位，经过图像预处理之后，进行重要特征点Gabor一Fisher的特征提取
和分类识别。-Video search, face recognition and implementation of key technologies. In this paper, face detection and recognition technology has been studied to realize an automatic face recognition for video search system. The system of input video frames for face detection and location, after image pre-processing carried out following important features of a Fisher-point Gabor feature extraction and classification and recognition. Platform: |
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Author:王帅 |
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Description: Head Rotation Estimation Algorithm for Hand-Free Computer Interaction by Rafał Kozik-In the article robust method of hands-free interaction with computer is proposed and tested. There are showed the results of algorithms based on optical flow and rapid face detection. Platform: |
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Author:Ng Jack |
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Description: 编程主要用于脸部特征提取，而且是三维图像。-Multilinear principal component analysis algorithm for face feature extraction. Platform: |
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Author:tai shu worn |
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Description: Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bayes classifier
that classifies an edge-extracted representation of an image. Using
edge representation significantly reduces the model’s size to only
5184 B, which is 2417 times smaller than a comparable statistical
modeling technique, while achieving an 86.6 correct detection
rate under various lighting conditions. Lip feature extraction uses
the contrast around the lip contour to extract the height and width
of the mouth, metrics that are useful for speech filtering. The
proposed FPGA system occupies only 15 050 logic cells, or about
six times less than a current comparable FPGA face detection
system.-Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bayes classifier
that classifies an edge-extracted representation of an image. Using
edge representation significantly reduces the model’s size to only
5184 B, which is 2417 times smaller than a comparable statistical
modeling technique, while achieving an 86.6 correct detection
rate under various lighting conditions. Lip feature extraction uses
the contrast around the lip contour to extract the height and width
of the mouth, metrics that are useful for speech filtering. The
proposed FPGA system occupies only 15 050 logic cells, or about
six times less than a current comparable FPGA face detection
system. Platform: |
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Author:ramanaidu |
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Description: 2dpca源代码，用于人脸识别，直接作用于矩阵提取特征。-2dpca the source code for face recognition, feature extraction directly on the matrix. Platform: |
Size: 2048 |
Author:gu |
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Description: 首先进行小波变换，在此基础上进行pca特征提取，在进行lda特征提取，用于人脸识别-First, wavelet transform, in this based on pca feature extraction, feature extraction during lda for face recognition Platform: |
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Author:gu |
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Description: 通过KL变换实现特征提取从而达到表情识别以及人脸识别的目的-By KL Transform feature extraction so as to achieve the purpose of face recognition and face recognition Platform: |
Size: 2048 |
Author:hefangfang |
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Search - face feature extraction