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基于统计模型的人脸识别pca程序
- 统计模式的人脸识别程序，准确度高，MATLAB编写。-statistical model of face recognition procedures, high accuracy, MATLAB prepared.
Face_Detection_In_RGB_Model
- 一个基于RGB模式的人脸检测MATLAB源代码，希望对热衷于人脸识别方面的朋友有所帮助。-an RGB model based on the Face Detection MATLAB source code, want to Face Recognition keen on the friends help.
20081022
- 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 Neur
FaceEvaluator
- A full implementation of ICA,PCA,LDA,SVM,in both orginal and incremental in model of real time learnign for face recognition.
1
- 基于隐马尔可夫模型的人脸识别CC++源代码图像识别部分的程序，-Face Recognition Based on Hidden Markov Model CC++ source code for the image recognition part of the process,
axvface
- 阿须人脸检测程序 1.0,人脸识别定位的示范程序-A face detection procedures to be 1.0, face recognition model program targeting
HMM
- This paper presents a hybrid framework of feature extraction and hidden Markov modeling (HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. T
data-mining-application-research
- 数据 挖掘就是面对海量的存储数据建立数学模型，找出隐含的业务规则和有价值的信 息，在实际应用中发挥作用 -Data mining is the face of vast amounts of stored data to a mathematical model to identify the implicit business rules and valuable information to play a role in practical applications
Face-orientation-recognition
- 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
Face-orientation-recognition
- LVQ即学习向量量化神经网络是一种用于训练竞争层的有监督学习方法神经网络，在模式识别和优化领域有着广泛的应用。本课题要求使用LVQ神经网络训练人脸的特征数据，得到模型对任一人脸图像的朝向进行识别。-Learning Vector Quantization LVQ neural network that is used to train competitive layer neural network supervised learning methods in the field of patt
LVQ-neural-network-prediction
- LVQ神经网络的预测——人脸朝向识别，数学建模经典模型-LVQ neural network prediction- Face towards the recognition, the classical model of mathematical modeling
face-recognition
- 基于神经网络的自动人脸识别模型，研究利用神经网络进行人脸识别的性能，通过与其它分类算法比较，为将来更进一步的人脸识别算法研究或者软件设计提供设计依据。-Automatic face recognition based on neural network model to study the use of neural network recognition performance, by comparison with other classification algorithms, provi
FaceRecognition_CNN(olivettifaces)
- 将CNN应用于人脸识别的流程，程序基于Python+numpy+theano+PIL开发，采用类似LeNet5的CNN模型，应用于olivettifaces人脸数据库，实现人脸识别的功能-CNN is applied to the process of face recognition. The program is based on Python+ numpy+ theano+ PIL development, and uses CNN model like LeNet5, which is
rd771
- 包含CV、CA、Single、当前、恒转弯速率、转弯模型，采用累计贡献率的方法，人脸识别中的光照处理方法。- It contains CV, CA, Single, current, constant turn rate, turning model, The method of cumulative contribution rate Face Recognition light treatment method.
funpiuhui
- 是学习PCA特征提取的很好的学习资料，数据模型归一化，模态振动，Gabor小波变换与PCA的人脸识别代码。- Is a good learning materials to learn PCA feature extraction, Normalized data model, modal vibration, Gabor wavelet transform and PCA face recognition code.
卷积神经网络详述
- 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 neur
FaceTools-master
- 本次作业使用的是汤晓鸥团队的DeepID第一代的网络，通过Caffe训练模型参数，经过LFW二分类得到人脸识别准确率。(This job is used by the Tang Xiaoou team's DeepID first generation network. Through the Caffe training model parameters, the accuracy of face recognition is obtained through the LFW two clas
shape_predictor_81_face_landmarks-master
- 基于dlib库的人脸68个特征点训练模型的扩展模型，可识别出人脸81个特征点，包括额头部分。(Based on the extended model of the face training model of 68 feature points based on Dlib database, 81 feature points can be recognized, including the forehead part.)
深度学习mtcnn
- 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 n
CNN-figure-recognize
- 本程序主要包括：人脸识别、图像采集、模型训练等功能(Face recognition, image acquisition, model training)