搜索资源列表
aamlibrary_release_2.0
- 基于AAM的人脸模型的训练,并可以实现人脸的拟合,在opencv基础上开发。-AAM(Active Appearance Models)source code。It is developped under OpenCV 1.0 for locating facial features。
HarrTraining
- harr训练的.net源码,haartraining 源代码,可用于人脸识别和行人识别等-harr,.net,sourcecode
Desktop
- 读入ORL人脸库的指定数目的人脸前前五张(训练) 输入:nFacesPerPerson --- 每个人需要读入的样本数,默认值为 5 nPerson --- 需要读入的人数,默认为全部 40 个人 bTest --- bool型的参数。默认为0,表示读入训练样本(前5张);如果为1,表示读入测试样本(后5张)-Read into the ORL face database face before the specified number of five sheets (
faces.tar
- 这是我在卡耐基梅隆大学图片数据库下的图片。19*19的数千张包括人脸和非人脸的pgm格式的图片。用来做人脸识别的训练和测试足够了。好资源-This is my picture database at Carnegie Mellon University under the picture. 19* 19 thousands, including face and non-human face of the pgm format. Used for training and testing fac
Project1
- 人脸图片的预处理,归一化,纳入数据库,训练以及识别-Face image preprocessing, normalization, into the database, training and recognition
fdp5finalMatlab.tar
- 这是一个使用了Gabor特征提取和人工智能的人脸检测系统源代码 使用步骤: 1. 拷贝所有文件到MATLAB工作目录下(确认已经安装了图像处理工具箱和人工智能工具箱) 2. 找到"main.m"文件 3. 命令行中运行它 4. 点击"Train Network",等待程序训练好样本 5. 点击"Test on Photos",选择一个.jpg图片,识别。 6. 等待程序检测出人脸区域 createffnn.m, d
jiyu-PCA-de-ren-lian-shi-bie-Matlab
- 基于主成分分析实现对特征脸的人脸识别,训练,输出识别率等。-Based on principal component analysis to achieve the characteristics of face recognition, training, recognition rate of the output.
train
- 进行svm分类器的训练,作为人脸检测的人脸分类器-For svm classifier training as face detection face classifier
image_identify
- 对人脸图像库的训练素材进行主成分分析,从而识别人脸图像。较详细说明见程序内部。-Face image database on the training material for the principal component analysis to identify the face image. See more details within the program.
FaceRecognition
- 基于特征向量方法的人脸识别。matlab实现。注意区分训练数据和测试数据。由于版权原因,代码包中不含测试任何图像。-Face recognition by eigenvector method. Class project.
112[1]
- 人脸检测根据姿态评估,然后利用adaboost方法训练分类器,是一片值得收藏的文章-Face detection based on posture assessment and classification using adaboost training method, is a worthy collection of articles
ProfileFace10
- 训练好的人脸正面分类器,可直接载入运行,-Positive face trained classifier, can be directly loaded run
facedetec-vcPP
- 训练好的人脸分类器,可直接载入运行,基于adaboost的级联分类器-Trained face classifier, can be directly loaded to run, based on cascade classifier adaboost
PCA_FaceRecognition
- 人脸识别算法,包括特征空间的训练,特征脸的形成-Face recognition algorithms, including the training feature space, the formation of facial features
PCA-pro
- 整个程序是基于Yale人脸数据库的PCA算法。算法主要分成三个部分。第一个部分是选择了每类图片的八张进行训练,形成基空间。第二部分是画图,主要是怎么画出特征脸,就是那个看着比较诡异的东西。可以修改数据,程序中提供了100个特征值和16个特征值的情况示例。最后一部分就是测试部分,检测命中率。效果很理想。-The whole process is based on the Yale face database PCA algorithm. Algorithm is divided into thre
face-detection
- 一个用神经网络进行人脸检测的程序,解压后运行main.m文件,之后对神经网络进行训练,需要一定的时间,耐心等待,最大400个周期,然后就可以对灰度人脸图像进行检测了。 -A neural network with a face detection program, run the main.m file after decompression, the neural network after training, take time, patience, maximum 400 cycles
orl-eye-database
- orl人脸库中截取出来的眼睛样本,作为疲劳检测中的人眼训练样本-orl face database from the eyes of the interception of the sample, as the fatigue test samples in the training of the human eye
face-detector
- 人脸检测的PCA算法。先利用PCA算法,将测试集在人脸空间中进行训练,得到人脸空间的基向量,再用试验图片进行试验。-PCA algorithm for face detection. First use of PCA algorithm, the test set in the face space for training, get face space basis vectors, and then test picture test.
FaceDetect_V1.3
- windows下的人脸检测的例程,适合研究人脸算法的人,包括训练实现-the windows face detection routine for researchers who face algorithm
How-to-train-their-classifier-OpenCV
- matlab图像特征识别。分类器的训练方法。很好的学习资料。如何用OpenCV训练自己的分类器。内含人脸库共训练器使用-matlab image feature recognition. Classifier training methods. Good learning materials. How to use OpenCV train their own classification. Training face database containing a total of uses