baselines.rst



Executing Baseline Algorithms
The first thing you might want to do is to execute one of the baseline face recognition algorithms that are implemented in bob.bio.

Setting up your Database
As mentioned in the documentation of :ref:`bob.bio.base <bob.bio.base>`, the image databases are not included in this package, so you have to download them.
For example, you can easily download the images of the `AT&T database`_, for links to other utilizable image databases please read the :ref:`bob.bio.face.databases` section.
By default, bob.bio does not know, where the images are located.
Hence, before running experiments you have to specify the image database directories.
How this is done is explained in more detail in the :ref:`bob.bio.base.installation`.

Running Baseline Experiments
To run the baseline experiments, you can use the baselines.py script by just going to the console and typing:

$ baselines.py


This script is a simple wrapper for the verify.py script that is explained in more detail in :ref:`bob.bio.base.experiments`.
The baselines.py --help option shows you, which other options you have.
Here is an almost complete extract:


--database: The database and protocol you want to use.
By default this is set to the image database atnt.

--algorithms: The recognition algorithms that you want to execute.
By default, only the eigenface algorithm is executed.

--all: Execute all algorithms that are implemented.

--temp-directory: The directory where temporary files of the experiments are put to.

--result-directory: The directory where resulting score files of the experiments are put to.

--evaluate: After running the experiments, the resulting score files will be evaluated, and the result is written to console.

--dry-run: Instead of executing the algorithm (or the evaluation), only print the command that would have been executed.

--verbose: Increase the verbosity level of the script.
By default, only the commands that are executed are printed, and the rest of the calculation runs quietly.
You can increase the verbosity by adding the --verbose parameter repeatedly (up to three times).

Usually it is a good idea to have at least verbose level 2 (i.e., calling baselines.py --verbose --verbose, or the short version baselines.py -vv).

Running in Parallel
To run the experiments in parallel, as usual you can define an SGE grid configuration, or run with parallel threads on the local machine.
For the baselines.py script, the grid configuration is adapted to each of the algorithms.
Hence, to run in the SGE grid, you can simply add the --grid command line option, without parameters.
Similarly, to run the experiments in parallel on the local machine, simply add a --parallel <N> option, where <N> specifies the number of parallel jobs you want to execute.
When running the algorithms from the :ref:`bob.bio.gmm <bob.bio.gmm>` package in parallel, the specialized scripts are executed.
This will speed up the training of the UBM (and possible additional steps) tremendously.

The Algorithms
The algorithms present an (incomplete) set of state-of-the-art face recognition algorithms. Here is the list of short-cuts: