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[Other resource] NeuroFaceDemo DL : 0: Face Recognition Demo using Neural Networks-Face Recognition Demo using Neural Network ks
Date : 2008-10-13 Size : 3.29mb User : 李琳莉
[AI-NN-PR] NeuroFaceDemo DL : 0: Face Recognition Demo using Neural Networks-Face Recognition Demo using Neural Network ks
Date : 2025-07-11 Size : 3.29mb User : 李琳莉
[AI-NN-PR] neuralnetwork_face_recognize DL : 0: 用人工神经网络进行人脸识别-using artificial neural networks face recognition
Date : 2025-07-11 Size : 1.71mb User : xz
[File Format] FACERECOGNITIONBASEDONFRACTALANDGENETICALGORITHMS. DL : 0: 本文的题目是基于分形和遗传算法的人脸识别方法，对有限人群提出一种采用分形特征和遗传聚类的识别方法: 将图像分成很多小区域, 分别计算各个区域的分形特征, 以充分利用图像二维信息同一个模式有多个样本, 通过遗传算法进行聚类以得到最优解实现不变性识别. 最后采用ORL 人脸图像库的一组图像对比了新方法、本征脸法和自联想神经网络方法, 结果表明该方法的识别率, 与本征脸法相似, 比自联想神经网络高.-The title of this article is based on fractal and genetic algorithms for face recognition method, a crowd of limited use of fractal characteristics and the identification of genetic clustering methods: the image is divided into many small regions, each region were calculated fractal characteristics, to take full advantage of two-dimensional image information with a model for a number of samples, through the genetic clustering algorithm in order to obtain the optimal solution to achieve invariant recognition. Finally, using ORL face image database of a group of image contrast of the new methods, eigenface law and auto-associative neural network methods, results show that the method of recognition rate, with the eigenface method is similar to auto-associative neural network than high.
Date : 2025-07-11 Size : 372kb User : 阳关
[AI-NN-PR] FaceRecognition DL : 0: 一个用神经网络方法实现人脸识别的程序，来源于CMU的machine learning 课程作业，具有参考价值-A method using neural network face recognition procedures derived from the machine learning courses CMU operations, reference value
Date : 2025-07-11 Size : 582kb User : 小威
[AI-NN-PR] face DL : 0: 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.
Date : 2025-07-11 Size : 14.88mb User : 刘新顺
[Graph Recognize] dctannprotected DL : 0: 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 the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT 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 DCT-based face recognition much faster than other approaches.-High information redundancy and correlation in face images result in inefficiencies 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 the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT 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 DCT-based face recognition much faster than other approaches.
Date : 2025-07-11 Size : 25kb User : mhm
[Other] waveannprotected DL : 0: 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.
Date : 2025-07-11 Size : 21kb User : mhm
[AI-NN-PR] v3.3 DL : 0: 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
Date : 2025-07-11 Size : 3.2mb User : yaxuan
[2D Graphic] face_recognition_PCAPBPNN DL : 0: 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 Neural Network (BPNN).
Date : 2025-07-11 Size : 2.97mb User : amine
[AI-NN-PR] pca-som DL : 0: 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.
Date : 2025-07-11 Size : 3.36mb User : 王超
[AI-NN-PR] SPIJ-37 DL : 0: Face recognition using artificial neural network
Date : 2025-07-11 Size : 989kb User : Rasha
[Other] facail-recognition-based-on-BP DL : 0: 基于BP神经网络的人脸识别系统的研究采用的是PCA和BP网络-Based on BP neural network face recognition system using PCA and BP Neural Network
Date : 2025-07-11 Size : 216kb User : 张正峰
[Other] BP-face-recognization DL : 0: 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.
Date : 2025-07-11 Size : 8.14mb User : 刘娇月
[Other] pxc3871260 DL : 0: 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 Transform and Principal Component Analysis using Neural Network
Date : 2025-07-11 Size : 227kb User : 胡冲
[AI-NN-PR] Face-orientation-recognition DL : 0: 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 samples reuse create LVQ neural network model, which is trained using training samples to adjust the weights using the test sample towards the establishment of a human face recognition model validated requires a higher recognition rate. This topic requires the use of LVQ neural network algorithm Matlab simulation, the human face towards effective judgment and identification.
Date : 2025-07-11 Size : 129kb User : 吴军
[Other] Untitled_1 DL : 0: 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提出了基于Gamma灰度矫正的光照预处理方法,并且在光照估计模型的基础上，进行相应的光照补偿和光照平衡策略。优化的形变统计校正理论基于统计形变的校正理论，优化人脸姿态；神经网络识别强化迭代理论强化迭代理论是对DLFA人脸检测算法的有效扩展；-Human Face Recognition
Date : 2025-07-11 Size : 2kb User : ok2007302593
[Special Effects] face DL : 0: 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
Date : 2025-07-11 Size : 5.34mb User : 李波
[matlab] BPN_based_Face_Recongition DL : 0: This code recognise face using back propgation neural network.
Date : 2025-07-11 Size : 386kb User : anika
[Windows Develop] Face Recognition DL : 0: Face Recognition and Verification Using Artificial Neural Network
Date : 2025-07-11 Size : 200kb User : HansanaA

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