Eigenfaces Group - Code

For our implementation, we chose the language Python, which is a dynamically-typed, object-oriented, high level interpreted language with an extensive toolkit. Add the Numerical Extensions and the Imaging Toolkit and you have all the primitives we need. In my opinion, the result is much friendlier to program than Matlab, as well as more flexible and probably faster (the linear algebra part of the Numerical Extensions just hands the work off to LAPACK).

• eigenlib.py (130 lines) - Provides the core functionality, including construction of eigenfaces, the transformation to face space, the inverse transform, and file I/O operations, but no command-line interface.
• constr.py (10 lines) - Front-end for eigenface construction.
• learn.py (22 lines) - Front-end for recording face vectors for later identification and reconstruction.
• recon.py (18 lines) - Front-end for converting a recorded face vector back into image space for viewing.
• identify.py (35 lines) - Front-end for comparing new images to recorded face vectors. Gives top three guesses as to who it is, as well as the "is a face?" score, which reflects the quality of the reconstruction.
• progressive.py (22 lines) - Front-end for building reconstructions using 1, then 2, then 3, ... up to all of the eigenfaces. These can be used to build a "reconstruction animation".
• pool.py (31 lines) - A relatively unsuccessful experiment. The idea was to take multiple picture of each person to compute an average face vector for that person. The was no noticeable improvement in recognition rates (not that there was much room for one).