PCA_based Face Recognition System_PCA_人脸识别_matlab

'Eigenface' Face Recognition System Written by: Amir Hossein Omidvarnia Email: [email protected] This package implements a well-known PCA-based face recognition method, which is called 'Eigenface' [1].此包实现一个众所周知的基于PCA的人脸识别 方法，即所谓'特征脸'[1]。 All functions are easy to use, as they are heavy commented. 所有功能都易于使用，因为他们是沉重的评论。 Furthermore, a sample script is included to show their usage. 此外，包含示例脚本以显示其用途。 In general, you should follow this order: 1. Select training and test database paths.选择的培训和测试数据库路径。 2. Select path of the test image.选择路径的测试图像。 3. Run 'CreateDatabase' function to create 2D matrix of all training images.运行'CreateDatabase'函数来创建的二维图像矩阵的所有培训。 4. Run 'EigenfaceCore' function to produce basis's of facespace.运行'EigenfaceCore'功能的基础上产生的facespace。 5. Run 'Recognition' function to get the name of equivalent image in training database. 运行'承认'函数来获取数据库..培训的同等图像的名称 For your convenience, I have prepared sample training and test databases, which are parts of 'face94' Essex face database [2]. You just need to copy the above functions, along with the training and test databases into a specified path (for example 'work' path of your MATLAB root). Then follow dialog boxes, which will appear upon running 'example.m'. 为方便起见，我准备样本训练和测试数据库，这是件 对'face94'埃塞克斯人脸数据库[2]。你只需要复制上述职能，随着 培训和到指定的路径（例如'测试数据库的工作'的路径 MATLAB的根）。然后按照对话框，它会自动显示后，运行'example.m'。 Enjoy it! References: [1] P. N. Belhumeur, J. Hespanha, and D. J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In ECCV (1), pages 45--58, 1996. [2] Available at: http://cswww.essex.ac.uk/mv/allfaces/faces94.zip