文件列表:

LICENSE (1104, 2018-11-26)
MATLAB (0, 2018-11-26)
MATLAB\CreateDatabase.m (2029, 2018-11-26)
MATLAB\ICA (0, 2018-11-26)
MATLAB\ICA\fastica.m (15824, 2018-11-26)
MATLAB\ICA\fpica.m (32957, 2018-11-26)
MATLAB\ICA\pcamat.m (12732, 2018-11-26)
MATLAB\ICA\remmean.m (460, 2018-11-26)
MATLAB\ICA\whitenv.m (2883, 2018-11-26)
MATLAB\LDA (0, 2018-11-26)
MATLAB\LDA\FisherfaceCore.m (3534, 2018-11-26)
MATLAB\PCA (0, 2018-11-26)
MATLAB\PCA\EigenfaceCore.m (2372, 2018-11-26)
MATLAB\PCA\EigenfaceCore_SVD.m (330, 2018-11-26)
MATLAB\Recognition.m (977, 2018-11-26)
MATLAB\face_rec.m (2499, 2018-11-26)
Makefile (953, 2018-11-26)
scripts (0, 2018-11-26)
scripts\convert-images.sh (615, 2018-11-26)
scripts\create-sets.py (2736, 2018-11-26)
scripts\cross-validate.py (2724, 2018-11-26)
scripts\datasets.py (3730, 2018-11-26)
scripts\face-det (0, 2018-11-26)
scripts\face-det\face-det.py (2413, 2018-11-26)
scripts\face-det\haarcascade_eye_tree_eyeglasses.xml (601661, 2018-11-26)
scripts\face-det\haarcascade_frontalface_alt.xml (676709, 2018-11-26)
scripts\get_feret.sh (961, 2018-11-26)
scripts\get_mnist.py (2142, 2018-11-26)
scripts\get_orl.sh (298, 2018-11-26)
scripts\get_yale.sh (690, 2018-11-26)
scripts\parse_gtex.py (2441, 2018-11-26)
scripts\pbs (0, 2018-11-26)
scripts\pbs\algorithm.sh (1171, 2018-11-26)
scripts\pbs\feret-hyperparameters.pbs (1358, 2018-11-26)
scripts\pbs\feret-ica-n1.pbs (498, 2018-11-26)
scripts\pbs\feret-ica-n2.pbs (498, 2018-11-26)
scripts\pbs\feret-ica-nonl.pbs (473, 2018-11-26)
... ...

# Face Recognition This repository contains the code for the face recognition system developed by the FACE creative inquiry. We are developing an accelerated, real-time recognition system based on several popular face recognition techniques. ## Installation Ubuntu (or some other Linux distribution) is the easiest and most recommended OS to use for this project. If you don't have a a Linux distribution, you have a few options: (1) set up a dual-boot, (2) set up a virtual machine, or (3) use a remote Linux machine (such as the Palmetto cluster) through SSH. Once you have installed all dependencies listed below, you can build this project with `make`. On Ubuntu, you can install several dependencies through `apt-get`: ``` sudo apt-get install cmake gcc gfortran git libopencv-dev ``` On Palmetto, these dependencies are available as modules: ``` module add cmake/3.6.1 cuda-toolkit/8.0.44 gcc/4.8.1 git opencv/2.4.9 ``` ### mlearn [mlearn](https://github.com/CUFCTL/mlearn) is a machine learning library that was spawned from this project. Clone this repo and follow the instructions in the README to build the library. ### OpenCV for Python If you want to use the `face-crop` tool, you will need to install the Python package for OpenCV: ``` sudo apt-get install python-opencv ``` ## Usage This repository includes a `face-rec` executable, which performs training, testing, and real-time recognition, and several helper scripts for getting datasets, running experiments, and face cropping. The examples below use the ORL database, which you can set up with `./scripts/get_orl.sh`. ### Training and Testing Run `./face-rec` without any arguments to view all of the options. To train and test the system with PCA and a 70/30 dataset partition: ``` ./scripts/create-sets.py -d orl -t 70 -r 30 ./face-rec --train train_data --test test_data --feat pca --loglevel=3 ``` ### Cross Validation To train and test the system 5 items with PCA and 70/30 dataset partitions: ``` ./scripts/cross-validate.py -d orl -t 70 -r 30 -i 5 -- --pca ``` Notice that arguments for `./face-rec` must be placed after a `--`. You can supply any valid arguments for `./face-rec in this way: ``` ./scripts/cross-validate.py -d orl -t 70 -r 30 -i 5 -- --pca --pca_n1=20 --loglevel=3 ``` ### Real-time Face Recognition To create a database and perform real-time recognition on a video stream: ``` ./scripts/create-sets.py -d orl -t 100 ./face-rec --train train_data --feat pca ./face-rec --stream --feat pca ``` `face-rec` will use the default video stream and perform face detection and recognition on each video frame in real time.

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