This project is a C++ implementation of the Fisherfaces method as described in:
- P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711--720, 1997.
It was written in preparation of libfacerec and its contribution to OpenCV. The project has no other dependencies except a recent OpenCV2.
All code is put under a BSD license, so feel free to use it for your projects:
Building the Project
This project does not need any external libraries except OpenCV, so compiling the project is as easy as writing (assuming you are in this folder):
philipp@mango:~/some/dir/lda$ mkdir build
philipp@mango:~/some/dir/lda$ cd build
philipp@mango:~/some/dir/lda/build$ cmake ..
philipp@mango:~/some/dir/lda/build$ make
philipp@mango:~/some/dir/lda/build$ ./lda filename.ext
And if you are in Windows using MinGW it may look like this:
C:\some\dir\lda> mkdir build
C:\some\dir\lda> cd build
C:\some\dir\lda\build> cmake -G "MinGW Makefiles" ..
C:\some\dir\lda\build> mingw32-make
C:\some\dir\lda\build> lda.exe filename.ext
You probably have to set the OpenCV_DIR variable if it hasn't been added by your installation, see Line 5 in the CMakeLists.txt on how to do this.
Using the Project
The project comes with an example, please have a look at the main.cpp on how to use the classes. You need some data to make the examples work, sorry but I really can't include those face databases in my repository. I have thoroughly commented the code and reworked it lately, to make its usage simpler. So if anything regarding the classes is unclear, please read the comments.
In the example I use a CSV file to read in the data, it's the easiest solution I can think of right now. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a filename followed by a ; (semicolon) followed by the label (as an integer number), making up a file like this:
/dataset/subject1/image0.jpg;0
/dataset/subject1/image1.ext;0
/dataset/subject2/image0.ext;1
/dataset/subject2/image1.ext;1
Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label. An example CSV file for the AT&T Facedatabase is given here, which looks like this (assuming I've extracted the database to /home/philipp/facerec/data/at/, file is without ... of course):
/home/philipp/facerec/data/at/s1/1.pgm;0
/home/philipp/facerec/data/at/s1/2.pgm;0
...
/home/philipp/facerec/data/at/s2/1.pgm;1
/home/philipp/facerec/data/at/s2/2.pgm;1
...
/home/philipp/facerec/data/at/s40/1.pgm;39
/home/philipp/facerec/data/at/s40/2.pgm;39
Once you have a CSV file with valid filenames and labels, you can run the demo by simply starting the demo with the path to the CSV file as parameter:
./lda /path/to/your/csvfile.ext
Or if you are in Windows:
lda.exe /path/to/your/csvfile.ext