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Face Recognition Demo using Neural Networks-Face Recognition Demo using Neural Network ks
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使用人工神经网络方法做人脸识别，基于c++卡法工具。-Using artificial neural network method to do face recognition.
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High information redundancy and correlation in face images result in efficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of t
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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 th
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毕业设计中 用VC++写的基于神经网络的人脸识别程序 优秀毕业设计 可供毕业设计学生和相关研究人员参考-Graduation written using VC++ face recognition program based on neural network design for outstanding graduate students and graduate design research Reference
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The purpose of this work is to identify a given face image using main features of face. The dimensionality of face image is reduced by the Principal component analysis (PCA, using eigenfaces method) and the recognition is done by the Back propagation
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matlab下采用som神经网络算法进行100个人脸识别，可扩展性强。-som neural network using matlab under 100 face recognition algorithm, scalability.
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Face recognition using artificial
neural network
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基于BP神经网络的人脸识别算法的MALAB实现。采用了抽样-全样训练的方式。-BP neural network-based face recognition algorithm MALAB. Using a sample- the whole kind of training methods.
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人脸识别论文
An Efficient Method for Face Feature Extraction and
Recognition based on Contourlet Transform and Principal
Component Analysis using Neural Network -An Efficient Method for Face Feature Extraction and
Recognition based on Contourlet T
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本课题研究的步骤如下：先提取人脸的特征向量；产生训练样本和测试样本；再用LVQ创建神经网络模型，该模型用训练样本进行训练调整权值；用测试样本对建立的人脸朝向识别模型进行验证，要求有较高的识别率。
本课题要求使用LVQ神经网络的算法进行Matlab仿真，对人脸朝向进行有效的判断和识别。
-This study is the following steps: first extract facial feature vector generate training and testing
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人脸识别算法分类
基于人脸特征点的识别算法（Feature-based recognition algorithms）。
基于整幅人脸图像的识别算法（Appearance-based recognition algorithms）。
基于模板的识别算法（Template-based recognition algorithms）。
利用神经网络进行识别的算法（Recognition algorithms using neural network）。
基于光照估计模型理论
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用神经网络进行人脸识别啊，效果特别好Face recognition using neural network, effect is very good-Face recognition using neural network, effect is very good
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Face recognition using BPNN. Contains 1.Face recognition using Back propagation neural network (customize code) code using matlab. 2. Face recognition using Back propagation network (builtin) code using matlab.Project closed for now,Adeel Raza Azeemi
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基于BP神经网络的人脸识别,用matlab实现的-Based on BP neural network face recognition, using matlab to achieve
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通过神经网络利用matlab和python实现人脸识别(Face recognition using MATLAB and python through neural network)
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通过python第三方库和matlab神经网络工具箱实现人脸识别(Face recognition using MATLAB and python through neural network)
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This code recognise face using back propgation neural network.
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Face Recognition and Verification Using Artificial Neural Network
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用卷积神经网络做的人脸识别，准确率达到92.75%。(The accuracy of face recognition using convolution neural network is 92.75%.)
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