Description: 该程序是在vc环境下编写的bp神经网络c++类库，能够广泛应用于人脸检测以及动作监控。-The program environment in vc prepared bp neural network c++ Class library that can widely used in face detection and action monitoring. Platform: |
Size: 20480 |
Author:孙继开 |
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Description: 一个外国人写的人脸检测程序，用到svm,pca,神经网络,还不错-Written by a foreigner face detection procedure, used svm, pca, neural network, but also good Platform: |
Size: 194560 |
Author:谢朝 |
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Description: 很不错的小波神经网络人脸检测源码，与大家分享-Very good wavelet neural network face detection source code to share with you Platform: |
Size: 5120 |
Author:李坤 |
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Description: 基于人工神经网络的图像识别方法研究。基于神经网络的人脸检测研究。基于特征融合与神经网络的手写体数字识别技基于遗传神经网络的手写体数字识别研究术研究。基于遗传优化的神经网络的银行票据手写数字识别。一种改进的人工神经网络模型-Based on artificial neural network image recognition method. Neural Network Based Face Detection Research. Based on Feature Fusion and Neural Network Recognition of Handwritten Numerals Based on Genetic Neural Network Technology of handwritten numeral recognition technique research study. Optimization based on genetic neural network bank notes handwritten numeral recognition. An Improved Artificial Neural Network Model Platform: |
Size: 13884416 |
Author:cheng |
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Description: 基于VC的人脸检测系统源码，其中用到了神经网络的相关理论进行编程-VC-based Face Detection system source code, which uses a neural network theory to program Platform: |
Size: 223232 |
Author:sanjinzhi |
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Description: 《精通VisualC++数字图像处理模式识别技术及工程实践》介绍了模式识别和人工智能中的一些基本理论，以及一些相关的模型，包括贝叶斯决策、线性判别函数、神经网络理论、隐马尔可夫模型、聚类技术等，同时结合模式识别中的一些问题，比如字符识别、笔迹鉴定、人脸检测、车牌识别、印章识别以及遥感图片、医学图片处理等内容，从多种角度，介绍了解决这些问题的思路-" Proficient in VisualC++ digital image processing, pattern recognition technology and engineering practice," describes the pattern recognition and artificial intelligence in some of the basic theory, and a number of related models, including Bayesian decision-making, linear discriminant function, neural network theory, hidden Markov Cardiff model, clustering technologies, combined with pattern recognition in a number of issues, such as character recognition, handwriting identification, face detection, license plate recognition, seals, and remote-sensing image recognition, medical image processing and other content, from several angles, introduced ideas to solve these problems Platform: |
Size: 11152384 |
Author:吴晶 |
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Description: 针对彩色图像提出了一种基于肤色和模板的人脸检测方法, 由肤色分割、模板匹配和人工神经网验证3 部
分组成. 首先使用HS I 空间的肤色统计模型分割出可能包含人脸的区域, 然后使用平均脸模板匹配和人工神经
网验证的方法在这些区域中搜索人脸. 该方法将彩色图像的肤色信息和灰度图像的模板匹配及人工神经网分类
模型综合起来, 既极大地提高了速度, 又具有较强的鲁棒性. 实验结果表明, 该算法是快速而有效的.-For color images is presented based on color and template face detection methods, from the color segmentation, template matching and artificial neural network validation three parts. The first to use HS I split the statistical model of color space which might contain human face region, and then use the average face template matching and artificial neural network authentication methods in these areas in search human face. This method of color image color information and gray-scale image of the template matching and artificial neural network classification model together, not only greatly enhanced the speed, but also has strong robustness. The experimental results show that the algorithm is fast and effective. Platform: |
Size: 457728 |
Author:天使 |
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Description: 增长式卷积神经网络及其在人脸检测中的应用-Growth-type Convolution Neural Network and Its Application in Face Detection growth-type convolution neural network and its application in face detection growing type convolution neural network and its application in face detection Platform: |
Size: 1108992 |
Author:方云 |
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Description: 在对图像进行Gabor变换后使用BP神经网络进行人脸检测的MATLAB程序-Gabor transform in the image using BP neural network after the face detection process MATLAB program Platform: |
Size: 15183872 |
Author:lilei |
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Description: 对拍摄得到的驾驶员视频帧图像, 使用复合肤色模型检测人脸 通过自适应边缘检测、 图像增强等方法处理得到
特征图像, 经特征区域筛选, 依据人脸先验知识匹配得到最佳人眼对 提取眼部特征向量, 结合 LVQ神经网络进行模式
识别检测眼部状态, 为判断驾驶员是否处于疲劳状态提供判据。-Video shot by the driver of the frame, the use of composite skin model of face detection through adaptive edge detection, image enhancement approach to be characteristic image, the feature area selection, based on prior knowledge of face matching the best human eye extracted eye feature vectors, combined with LVQ neural network pattern recognition detection of eye condition, to determine whether driver fatigue is provided in the criterion. Platform: |
Size: 106496 |
Author:廖减员 |
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Description: 使用Gabor特征提取和神经网络的人脸检测，里面带有人脸和非人脸的训练图库，检测效果很好。
运行该程序：
1 -所有文件和目录复制到MATLAB的工作文件夹 *-为了运行程序，你必须有图像处理和神经网络工具箱
2 - 找到名为“main.m”的文件
3 - 双击这个文件或在命令窗口中的“主”类型
4 - 将显示一个菜单。点击“火车网”，并等待，直到程序完成培训
5 - 点击“照片上的测试”。将出现一个对话框。选择一个。JPG图片
6 - 等待，直到程序检测到一些面孔
-Gabor feature extraction and neural network face detection, which with a human face and non-face training Gallery detection works well. Run the program: 1- all files and directories copied to MATLAB work folder*- In order to run the program, you must have image processing and neural network toolbox- find a file named " main.m" - double-click on the file or " master" in the command window type- will display a menu. Click on the train network, and wait until the process is complete training- Click the photo test. A dialog box appears. Select one. JPG picture- wait until the program detects some faces Platform: |
Size: 19137536 |
Author:朝颜 |
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Description: This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot navigation and others (see references for more info). Because of some architectural features of convolutional networks, such as weight sharing it is imposible to implement it using Matlab Neural Network Toolbox without it s source modifications. Platform: |
Size: 627712 |
Author:郭志 |
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Description: 基于Gabor特征提取和神经网络的人脸检测的matlab程序-Human face detection based on Gabor feature extraction and neural network in matlab Platform: |
Size: 152576 |
Author:mark |
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Description: Face is a primary focus of attention in social intercourse, playing a major role in conveying identity and
emotion. The human ability to recognize faces is remarkable. People can recognize thousands of faces learned throughout
their lifetime and identify familiar faces at a glance even after years of separation. This skill is quite robust, despite large
changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses, beards or
changes in hair style. In this work, a system is designed to recognize human faces depending on their facial features. Also
to reveal the outline of the face, eyes and nose, edge detection technique has been used. Facial features are extracted in the
form of distance between important feature points. After normalization, these feature vectors are learned by artificial
neural network and used to recognize facial image. Platform: |
Size: 240640 |
Author:fatemeh |
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Description: gabor+神经网络实现人脸检测；可能会提示缺少data文件夹，自己在根目录下建一个就好了-gabor+ neural network face detection may be prompted to missing data folder in the root directory of built himself a nice Platform: |
Size: 216064 |
Author:carl |
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Description: 从卷积神经网络的发展历史开始，详细阐述了卷积神经网络的网络结构、神经元模型和训练算法。在此基础上以卷积神经网络在人脸检测和形状识别方面的应用为例，简单介绍了卷积神经网络在工程上的应用，并给出了设计思路和网络结构。(Starting from the history of the convolution neural network, the network structure, neuron model and training algorithm of the convolution neural network are elaborated in detail. On the basis of the application of convolutional neural network in face detection and shape recognition, this paper briefly introduces the application of convolution neural network in engineering, and gives the design idea and network structure.) Platform: |
Size: 531456 |
Author:longbing001
|
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Description: 用市面上的摄像头，可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and network layers, so that each layer can learn more features and greatly improve the generalization ability. The network is deeper, the amount of calculation in each layer is reduced, and the ability of feature extraction is strengthened, so as to improve the accuracy of target classification. On the LFW data set, the accuracy of face recognition reaches 98.40%. In this experiment, mtcnn is introduced into the face detection algorithm. Its backbone network is divided into three convolutional neural networks: p-net, R-Net and o-net. Among them, o-net is the most strict in screening candidate face frames. It will output the coordinates of a human face detection frame and five facial feature points (left eye, right eye, nose, left mouth corner, right mouth corner).) Platform: |
Size: 2415616 |
Author:莱尼 |
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Search - neural network in face detection