Description: 基于小波的人脸识别算法的实现。这是我们研究时写的代码，实际应用时可根据自己的需要做适当的调整改变。-Face recognition based on wavelet algorithm implementation. This is our study writing code, the actual application can be based on their own needs appropriate adjustments to change. Platform: |
Size: 4096 |
Author:智能算法 |
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Description: 一种基于离散小波变换和支持向量机的人脸识别新方法，是PDF格式的-based on the discrete wavelet transform and support vector machines Face Recognition new methods are in PDF format Platform: |
Size: 309248 |
Author:pylu[t |
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Description: 该文介绍了基于Gabor小波变换和支持向量机的人脸识别.-In this paper, based on Gabor Wavelet Transform and Support Vector Machines Face Recognition. Platform: |
Size: 131072 |
Author:Jacky |
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Description: 基于小波变换的主成分析在人脸识别中的应用MATLAB程序-Based on Wavelet Transform Analysis of Principal Application to Face Recognition MATLAB program Platform: |
Size: 2048 |
Author:关耳 |
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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: |
Size: 3935232 |
Author:hyh |
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Description: opencv摄像头实时人脸识别，支持各种摄像头-Real-time face recognition opencv camera, supports a variety of camera Platform: |
Size: 1180672 |
Author:cranechen |
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Description: 基于小波分析和主成分分析的人脸识别研究随着社会的发展，社会各个方面对快速有效的身份验证的要求日益迫切。由
于生物特征是人的内在属性，具有很强的自身稳定性和个体差异性，因此是身份
验证的理想依据。其中利用人脸特征又是最自然直接的手段，相比其他生物特征，
它具有直接、友好、方便的特点，易于为用户接受。从而，人脸识别吸引了越来
越多来自计算机视觉和信号处理等领域的关注，成为模式识别、图像处理等学科
的研究热点。-Based on wavelet analysis and principal component analysis of the Face Recognition With the social development of all aspects of society in his capacity as quickly and efficiently verify the requirements of increasingly urgent. Biological characteristics as a result of the inherent human attribute, has a strong individual differences in their stability and, therefore, is an ideal basis for authentication. The use of facial features which is the most natural means of direct, compared with other biological characteristics, it has a direct, friendly, convenient and easy for users to accept. Thus, face recognition has attracted more and more from the computer vision and signal processing and other areas of concern, as a pattern recognition, image processing, such as the research subjects. Platform: |
Size: 37888 |
Author:付采 |
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Description: 针对灰度图像，提出一种基于知识的人脸检测方法。
提出了一种给予支持向量机的人脸检测方法。
提出了一种基于小波分解的LDA人脸识别方法。
提出了一种基于小波和DCT的人脸识别方法。
提出了一种机遇CEDT和支持向量机的人脸分类和识别方法。
-For gray-scale images, a knowledge-based face detection methods. A support vector machine method of face detection. A wavelet decomposition of the LDA-based face recognition methods. A wavelet and DCT-based face recognition methods. A CEDT opportunities and support vector machine classification and face recognition. Platform: |
Size: 7113728 |
Author:yanyan |
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Description: 人脸识别因其在安全验证系统、信用卡验证、医学、档案管理、视频会
议、人机交互、系统公安(罪犯识别等)等方面的巨大应用前景而越来越成为
当前模式识别和人工智能领域的一个研究热点。
本文提出了基于24位彩色图像对人脸进行识别的方法，介绍的主要内容是图像处理，它在整个软件中占有极其重要的地位，图像处理的好坏直接影响着定位和识别的准确率。本软件主要用到的图像处理技术是：光线补偿、高斯平滑和二值化。在识别前，先对图像进行补光处理，再通过肤色获得可能的脸部区域，最后根据人脸固有眼睛的对称性来确定是否就是人脸，同时采用高斯平滑来消除图像的噪声，再进行二值化，二值化主要采用局域取阈值方法，接下来就进行定位、提取特征值和识别等操作。经过测试，图像预处理模块对图像的处理达到了较好的效果，提高了定位和识别的正确率- Face recognition is a complex and difficult problem that is important for surveillance and security, telecommunications, digital libraries , video meeting, and human-computer intelligent interactions.
The paper introduced the method of face recognition that based on the 24 bit multicolor image, Main content that the paper introduced is the picture treatment, It occupies the extremely important position in the whole software, the quality of picture process directly influenced the accuracy rate of localization and discerning. The picture process technology that the software mainly used included : light compensating、gauss smooth and twain value method. before discerning, we compensated the light for image, then we could obtain the possible face area through the complexion, finally, the system could depend on the symmetry of eyes to make sure whether it is the face of people, at the same time, the system could eliminate noises through the method that named gauss smoothness, then we use Platform: |
Size: 2286592 |
Author:张雨 |
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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: |
Size: 432128 |
Author:Swati |
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Description: 基于Gabor小波与RBF神经网络的人脸识别新方法-Gabor Wavelet Based Face Recognition with RBF Neural Networks A New Method Platform: |
Size: 240640 |
Author:Ailsa |
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Description: 基于小波变换的人脸识别代码本代码对图像进行小波分解，然后又用最近邻方法实现了图像的识别。-Face Recognition Based on Wavelet Transform code-wavelet based face recognition, the code of the image wavelet decomposition, and then with the nearest neighbor method to achieve the image recognition! Platform: |
Size: 1024 |
Author:田虎 |
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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: |
Size: 2048 |
Author:gu |
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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: Wavelet transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when Wavelet coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes Wavelet-based face recognition much more accurate than other approaches.
Platform: |
Size: 21504 |
Author:mhm |
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Description: One of Biometrics fields is face recognition & face expression recognition ...
1- In face recognition .. we need to design authentication program by training a neural network ,there are two source codes..one of them is based on Discrete Wavelet Transform with Perceptron Neural Network.. and the other based on Discrete Cosine Transform with Perceptron Neural Network ...
2- In face expression recognition .. we defined the condition of the person (nature,happiness,disgust or anger)
this source code is based on Principle component analysis(PCA) ..
* we need to now about digital image processing
,neural network and PCA-One of Biometrics fields is face recognition & face expression recognition ...
1- In face recognition .. we need to design authentication program by training a neural network ,there are two source codes..one of them is based on Discrete Wavelet Transform with Perceptron Neural Network.. and the other based on Discrete Cosine Transform with Perceptron Neural Network ...
2- In face expression recognition .. we defined the condition of the person (nature,happiness,disgust or anger)
this source code is based on Principle component analysis(PCA) ..
* we need to now about digital image processing
,neural network and PCA... Platform: |
Size: 11905024 |
Author:mahmoud |
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Search - wavelet based face recognition