搜索资源列表
fdtool
- 利用局部二位模式和haar特征进行人脸或目标识别。-This toolbox provides some tools for objects/faces detection using Local Binary Patterns (and some variants) and Haar features. Object/face detection is performed by evaluating trained models over multi-scan windows with
Face-recognition
- 基于SVM的人脸识别，识别率很高的，MATLAB代码，已验证完毕-SVM-based face recognition, recognition rate high, MATLAB code, has completed verification
Face_recognize
- matlab代码实现人脸检测识别，提取人脸图像的kpca特征利用图像匹配快速识别人脸。-matlab face recognition kpca svm
13FaceRec
- 人脸特征提取与识别matlab程序，主要提取了PCA特征、SVM分类和核方法分类等，代码可以直接使用-Face recognition based on PCA features and Kernel methods, which is used in pattern extraction.
FaceRec_SourceCode
- 基于PCA-SVM的人脸识别，平均识别率达83 ，是基于matlab开发的。-PCA-SVM-based face recognition, the average recognition rate of 83 , based on matlab development.
All-Files
- 用MATLAB实现基于主成分分析（PCA）和支持向量机（SVM）的人脸识别系统，打开运行FR_GUI函数即可，我放在E盘中的，注意一下路径，当前识别率一般，也欢迎交流指正1127851044@qq.com，谢谢。-Using MATLAB analysis (PCA) based on principal component analysis and support vector machine (SVM) face recognition system to open the run FR_G
FaceRec
- 人脸表情识别matlab程序PCA+SVM算法，SVM分类-orL人脸数据库有数据有图片-Facial expression recognition matlab program PCA+SVM algorithm, SVM classification-orL face
FacialExpressionClassification
- 1. 使用matlab自带的人脸识别工具（Viola-Jones算法）找出人脸的位置，并裁剪出人脸区域。 2. 使用Gabor滤波器识别出人脸的局部特征及纹理。 3. 训练一个SVM进行表情分类。 4. 交叉验证得到表情分类正确率为83.3 。 操作说明和系统描述请见ReadMe.-1. Using matlab with face detection tool (Viola-Jones algorithm) to find the location of a human
patturnpatternclassification
- 支持向量机方法,用matlab实现,用于分类检测,模式识别,人脸检测等-Support vector machine (SVM) method, matlab, used for classification, pattern recognition, face detection, etc
mejhnq-detection-matlab
- 支持向量机方法,用matlab实现,用于分类检测,模式识别,人脸检测等-Support vector machine (SVM) method, matlab, used for classification, pattern recognition, face detection, etc
dte
- 支持向量机方法,用matlab实现,用于分类检测,模式识别,人脸检测等-Support vector machine (SVM) method, matlab, used for classification, pattern recognition, face detection, etc