文件列表:

FaceRec_using_PCA_and_KNN\Demo.asv (17889, 2017-09-28)
FaceRec_using_PCA_and_KNN\Demo.m (17891, 2017-09-28)
FaceRec_using_PCA_and_KNN\getAvgFace.m (1007, 2017-03-25)
FaceRec_using_PCA_and_KNN\KNN_.m (2775, 2017-12-29)
FaceRec_using_PCA_and_KNN\load_data.m (2894, 2017-03-24)
FaceRec_using_PCA_and_KNN\PCA_.m (1842, 2017-03-24)
license.txt (1518, 2017-12-29)

Author: Mahmoud Afifi - mafifi@eecs.yorku.ca York University The files here are: (1) load_data: load the data from face_images.mat and nonface_images.mat face_images.mat file should contain: - train_imgs: NxMxL tensor that contains N test face images. Each image is MxL pixels (grayscale). - train_ids: Nx1 vector that contains the id of each image in test_imgs - test_imgs: KxMxL tensor that contains N test face images. Each image is MxL pixels (grayscale). - test_ids: Kx1 vector that contains the id of each image in test_imgs nonface_images.mat file should contain: - nonface_imgs: SxMxL tensor that contains S non-face images. Each image is MxL pixels (grayscale) (2) getAvgFace: calculate the average of the training face images and display it. (3) PCA_: calculate the principle components (PCs), the latent low-dimensional data, and the eigenvalues (4) KNN_: classifying using k-nearest neighbors algorithm. The nearest neighbors search method is euclidean distance. (5) Demo: is a demo! Note: you have to prepare your data as described in (1) To get the results: 1- Download the datasets and locate them in the same directory of the source code. 2- Run Demo.m

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