Description: 平台：VS2005
描述：这是华东师大模式识别课程的第三个Homework。用C#实现的人脸识别小程序，算法采用K阶近邻法，人脸图片来自Yale Database。上传的压缩文件里面有我的report和工程文件夹的打包。-Platform: VS2005 Description: This is a pattern recognition course ECNU third Homework. With C# Applet to achieve face recognition algorithm using K-order neighbor of law, human face images from the Yale Database. From there the compressed file my report and engineering package folder. Platform: |
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Author:luoxi |
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Description: PCA人脸识别 This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames-PCA Face Recognition This package implements basic Principal Component Analysis in Matlab andtests is with grayscale portion of the FERET database. Images are notpreprocessed and it is up to the user to preprocess the images as wanted, not changing the filenames Platform: |
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Author:蔡加欣 |
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Description: 使用Fisher线性鉴别分析（FLDA）方法在ORL人脸数据库上进行人脸识别试验。ORL标准人脸库共包含40人，每人10幅共400幅BMP图像。-The use of Fisher linear discriminant analysis (FLDA) at Ways on ORL face database for face recognition test. Standard ORL face database contains a total of 40 people, 10 per person a total of 400 BMP images. Platform: |
Size: 4096 |
Author:liz |
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Description: 此实验使用核Fisher鉴别分析（KFDA）方法在ORL人脸数据库上进行人脸识别试验。ORL标准人脸库共包含40人，每人10幅共400幅BMP图像。-This experiment the use of nuclear Fisher discriminant analysis (KFDA) method on ORL face database for face recognition test. Standard ORL face database contains a total of 40 people, 10 per person a total of 400 BMP images. Platform: |
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Author:liz |
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Description: 基于fisherface的人脸图像识别，采用lda降维的方法来识别人脸图像-This package implements a well-known FLD-based face recognition
method, which is called Fisherface [1].
All functions are easy to use, as they are heavy commented.
Furtheremore, a sample script is included to showe their usage.
In general, you should follow this order:
1. Select training and test database paths.
2. Select path of the test image.
3. Run CreateDatabase function to create 2D matrix of all training images.
4. Run FisherfaceCore function to produce basis s of facespace.
5. Run Recognition function to get the name of equivalent image in training database.
For your convenience, I have prepared sample training and test databases, which are parts
of face94 Essex face database [2]. You just need to copy the above functions, along with
the training and test databases into a specified path (for example work path of your
MATLAB root). Then follow dialog boxes, which will appear upon running examp Platform: |
Size: 262144 |
Author:paul |
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Description: 人脸识别中经常用到的orl数据库，库大小是每人10幅，共有40人的人脸图像-Face Recognition frequently be used orl databases, database size is 10 per person, a total of 400 face images Platform: |
Size: 4348928 |
Author:shizr |
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Description: Amir Hossein Omidvarnia用matlab编写的基于PCA的人脸识别系统和基于FLD的人脸识别系统,其中
的图像示例为Essex face database中 face94 的部分图像,文献可参考"Eigenfaces vs. Fisherfaces:
Recognition using class specific linear projection."已经测试过程序可正常运行没有问题。-Amir Hossein Omidvarnia prepared using matlab Face Recognition System Based on PCA and FLD-based face recognition systems, which sample the image of Essex face database for ' face94' part of images, documents may refer to " Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. " procedures have been tested there is no problem to normal operation. Platform: |
Size: 377856 |
Author:刘子木 |
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Description: YALE 人脸数据库，人脸识别的好工具，15个人，每个人11副图像-YALE face database, face recognition a good tool, 15 individuals, each 11 images Platform: |
Size: 1175552 |
Author:wj |
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Description: 机器学习中的人脸识别程序。内含人脸库，有linux和matlab两个版本。人脸图像均为灰度图像，方便大家学习。-Machine Learning in face recognition program. Containing face database, there are two versions of linux, and matlab. Face images are grayscale images, to facilitate them to learn. Platform: |
Size: 11938816 |
Author:lyn |
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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: |
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Author:费富里 |
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Description: ORL人脸图像库，共40人，每人10幅图像，其中每人的前5幅作为训练样本，后5幅作为测试分类样本，统计正确分类率。分类准则为最近邻规则。
真实的图像尺寸为112x92，列向量堆积对应人脸库矩阵的每一列。 -ORL face image database, a total of 40 per 10 images, each of which the first five as training samples, after the 5 categories as a test sample, correct classification rate statistics. Classification criteria for the nearest neighbor rule. The real image size is 112x92, the corresponding column vector face database matrix accumulation of each column. Platform: |
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Author:limei |
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Description: 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定
位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基
准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进
行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最
近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method. Platform: |
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Author:MJ |
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Description: 对人脸识别的贝叶斯方法ML中相似度计算公式进行了简化，对数据集的训练和人脸图像的预处理进
行了修改，提出了一种改进的贝叶斯人脸识另1】算法SML。在FERET人脸图像库的子集和南大人脸图像实验库上对
识别算法进行了测试和比较。实验表明，SML算法提高了ML算法的效率，克服了ML算法计算效率不高的缺陷，而
且SML的识别效率明显高于PCA方法。-Bayesian face recognition method on the ML in the similarity formula has been simplified, the data set of training and pre-processing face images were modified, an improved understanding of Bayesian Face Ling 1】 algorithm SML. In the FERET face image database a subset of the Southern adults face image recognition algorithm on the experimental database was tested and compared. Experiments show that, SML algorithm improves the efficiency of ML algorithm, ML algorithm overcomes the shortcomings of high efficiency, and the recognition rate significantly higher than SML PCA method. Platform: |
Size: 262144 |
Author:dmay |
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Description: 计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸
特征提取方法。-Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method. Platform: |
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Author:Highjoe |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 152576 |
Author:a v |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 357376 |
Author:a v |
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Description:
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 23552 |
Author:a v |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 47104 |
Author:a v |
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Description: 该人脸库包含30个人、每人10幅总共300幅光栅图像，每个人的10幅图像包括了朝正前方、朝左、朝右、朝上和朝下五种不同的视角
方向的情形，经典的多姿态人脸库
注意此文件为.ras格式，需要用ACDSEE打开，SUN光栅图片格式。-The face database contains 30 individuals, each 10 Total 300 raster images, 10 images of each person included toward the front, left, turn right, up and down direction of the perspective of five different situations, Classic Pose-face database Note that this file is. ras format, you need to open with ACDSEE, SUN raster image formats. Platform: |
Size: 63641600 |
Author:shirley |
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Search - face images database