A MATLAB toolbox for face classifier 1.0.7
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algorithm Version 1.0.6 Aug 3, 2017
auxiliary Version 1.0.6 Aug 3, 2017
dataset Versio 1.0.1 Jul 6, 2017
demo_example Version 1.0.6 Aug 4, 2017
lib Version 1.0.6 Aug 3, 2017
.dropbox Version 1.0.3 Jul 10, 2017
.gitignore Version 1.0.5 Jul 27, 2017
LICENSE Initial version. Jun 29, 2017
README.md Version 1.0.7 Sep 11, 2017
demo.m Version 1.0.7 Sep 11, 2017
demo_old.m Version 1.0.7 Sep 11, 2017
download.m 1.0.7 Jun 11, 2018
run_me_first.m Version 1.0.1 Jul 6, 2017

README.md

ClassifierToolbox : A Matlab toolbox for classifier.


Authors: Hiroyuki Kasai

Last page update: Seo. 11, 2017

Latest library version: 1.0.7 (see Release notes for more info)

Introduction

This package provides various tools for classification, e.g., image classification, face recogntion, and related applicaitons.

List of algorithms


Folders and files

./                              - Top directory.
./README.md                     - This readme file.
./run_me_first.m                - The scipt that you need to run first.
./demo.m                        - Demonstration script to check and understand this package easily. 
|algorithm/                     - Algorithms for classifcations.
|auxiliary/                     - Some auxiliary tools for this project.
|demo_examples/                 - Some demonstration files.
|lib/                           - 3rd party tools.
|dataset/                       - Folder where datasets are stored.

First to do: configure path

Run run_me_first for path configurations.

%% First run the setup script
run_me_first; 

Second to do: download datasets and external libraries

Run download for downloading datasets and external libraries.

%% Run the downloading script
download; 
  • If your computer is behind a proxy server, please configure your Matlab setting. See this.

Usage example: ORL face dateset demo: 3 steps!

Now, just execute demo for demonstration of this package.

%% Execute the demonstration script
demo; 

The "demo.m" file contains below.

%% load data
load('./dataset/AR_Face_img_60x43.mat');

%% set option
options.verbose = true;

%% LSR
[accuracy_lsr, ~, ~] = lsr(TrainSet, TestSet, train_num, test_num, class_num, 0.001, options);

%% LRC
accuracy_lrc = lrc(TrainSet, TestSet, test_num, class_num, options);

%% show recognition accuracy
fprintf('# LSR: Accuracy = %5.5f\n', accuracy_lsr);
fprintf('# LRC: Accuracy = %5.5f\n', accuracy_lrc);

Let take a closer look at the code above bit by bit. The procedure has only 3 steps!

Step 1: Load data

First, we load datasets including train set and test set.

load('./dataset/AR_Face_img_60x43.mat');

Step 2: Perform solver

Now, you can perform optimization solvers, i.e., LSR and LRC with appropriate paramters.

%% LSR
[accuracy_lsr, ~, ~] = lsr(TrainSet, TestSet, train_num, test_num, class_num, 0.001, options);

%% LRC
accuracy_lrc = lrc(TrainSet, TestSet, test_num, class_num, options);

Step 3: Show recognition accuracy

Finally, the final recognition accuracis are shown.

fprintf('# LSR: Accuracy = %5.5f\n', accuracy_lsr);
fprintf('# LRC: Accuracy = %5.5f\n', accuracy_lrc);

That's it!


License

  • This toobox is free, non-commercial and open source.
  • The code provided in this toobox should only be used for academic/research purposes.

Third party tools, libraries, and packages.


Problems or questions

If you have any problems or questions, please contact the author: Hiroyuki Kasai (email: kasai at is dot uec dot ac dot jp)


Release Notes

  • Version 1.0.7 (Sep. 11, 2017)
    • Fix bugs.
  • Version 1.0.6 (Aug. 03, 2017)
    • Add R-DR, R-SRC, others.
  • Version 1.0.5 (July 27, 2017)
    • Add JDDRDL and others.
  • Version 1.0.4 (July 11, 2017)
    • Add and modify SSRC etc.
  • Version 1.0.3 (July 10, 2017)
    • Add and modify SDR-SLR etc.
  • Version 1.0.2 (July 07, 2017)
    • Add and modify RSR, SVM etc.
  • Version 1.0.1 (July 06, 2017)
    • Add and modify many items.
  • Version 1.0.0 (July 01, 2017)
    • Initial version.