antispoofing.eyeblink 1.0.0

Using an automatic installer

Using pip is the easiest (shell commands are marked with a $ signal):

$ pip install antispoofing.eyeblink

You can also do the same with easy_install:

$ easy_install antispoofing.eyeblink

This will download and install this package plus any other required dependencies. It will also verify if the version of Bob you have installed is compatible.

This scheme works well with virtual environments by virtualenv or if you have root access to your machine. Otherwise, we recommend you use the next option.

Using zc.buildout

Download the latest version of this package from PyPI and unpack it in your working area. The installation of the toolkit itself uses buildout. You don’t need to understand its inner workings to use this package. Here is a recipe to get you started:

$ python bootstrap.py
$ ./bin/buildout

These 2 commands should download and install all non-installed dependencies and get you a fully operational test and development environment.

Note for Grid Users

At Idiap, we use the powerful Sun Grid Engine (SGE) to parallelize our job submissions as much as we can. At the Biometrics group, we have developed a little toolbox <http://pypi.python.org/pypi/gridtk> that can submit and manage jobs at the Idiap computing grid through SGE. If you are at Idiap, you can download and install this toolset by adding gridtk at the eggs section of your buildout.cfg file, if it is not already there. If you are not, you still may look inside for tips on automated parallelization of scripts.

The following sections will explain how to reproduce the paper results in single (non-gridified) jobs. A note will be given where relevant explaining how to parallalize the job submission using gridtk.

Calculate Frame Differences

The eye-blink detector calculates normalized frame differences like our face versus background motion detector at the antispoofing.motion package, except it does it for the eye region and face remainer (the part of the face that does not contain the eye region). In the first stage of the processing, we compute the eye and face remainder regions normalized frame differences for each input video. To do this, just execute:

$ ./bin/framediff.py /root/of/database /root/of/annotations results/framediff

There are more options for the framediff.py script you can use (such as the sub-protocol selection). Note that, by default, all applications are tunned to work with the whole of the replay attack database. Just type –help at the command line for instructions.

There is one parameter in special you may need tunning on the above script, which relates to the --maximum-displacement option. This option controls the percentage in eye-center movement in which the method still considers the current detection is valid, w.r.t. the previous frame. If the eye-center positions between the current and previous frame move more than the specified ratio of the eye-width, then the detection is considered invalid and is discarded.

Creating Partial Score Files

To create the final score files, you will need to execute make_scores.py, which contains a simple strategy for producing a single score per input frame in every video. The final score is calculated from the input eye and face remainder frame differences in the following way:

S = ratio(eye/face_rem) - running_average(ratio(eye/face_rem))

The final score is set to S, unless any of the following conditions are met:

1
  S < running_std_deviation(ratio(...))

2
  eye == 0

3
  S < running_average(ratio(...))

In these cases S is replaced by the output of running_average(ratio(...)).

To compute the scores S for every frame in every input video, do the following:

$ ./bin/make_scores.py --verbose results/framediff results/partial_scores

There are more options for the framediff.py script you can use (such as the sub-protocol selection). Note that, by default, all applications are tunned to work with the whole of the replay attack database. Just type –help at the command line for instructions.

We don’t provide a grid-ified version of this step because the job runs quite fast, even for the whole database.

Counting Eye-Blinks

The next step of the process is to use the partial scores for each video (a signal through time) to count the number of blinks perceived in every database element. You can use the count_blinks.py script for that:

$ ./bin/count_blinks.py --verbose results/partial_scores results/blinks

The output files will have integer values as scores for each frame, with the number of blinks accounted up to that point in time. These files can be used as score output files for fusion processes.

Merging Scores

If you wish to create a single 5-column format file by combining this counter-measure scores for every video into a single file that can be fed to external analysis utilities such as our antispoofing.evaluation <http://pypi.python.org/pypi/antispoofing.evaluation> package, you should use the script count_blinks.py. The merged scores represent the number of eye-blinks computed for each video sequence. You will have to specify how many of the scores in every video you will want to consider and the input directory containing the scores files that will be merged (by default, the procedure considers only the first 220 frames, which is some sort of common denominator between real-access and attack video number of frames).

The output of the program consists of a single 5-column formatted file with the client identities and scores for every video in the input directory. A line in the output file corresponds to a video from the database.

You run this program on the output of make_scores.py. So, it should look like this if you followed the previous example:

$ ./bin/merge_scores.py --verbose results/partial_scores results/blinks

The above commandline example will generate 3 text files on the results directory containing the training, development and test scores, accumulated over each video in the respective subsets. You can use other options to limit the number of outputs in each file such as the protocol or support to use.

There are two main options you may need to tweak on this program: --skip-frames and --threshold-ratio. The first one, --skip-frames, determines how many frames to skip between eye-blinks, to avoid multiple eye-blink detections on a single user blink (defaults to 10). The other parameter defines how many standard-deviations from the running mean, a given signal peak should be considered as originating from an eye-blink. It is set by default to 3.0.

Creating Movies

You can create animated movies showing the detector operation using the make_movie.py script. This script will combine all the above steps in the detection process and will generate a movie file showing the original input movie that is being analyzed, facial landmarks, the light normalization result and the resulting score evolution, together with instantaneous eye-blink thresholds. You can use it to debug the eye-blinking detector and better tune the parameters for batch processing. The script takes the full path to a movie file in the REPLAY-ATTACK database and an output movie filename:

$ ./bin/make_movie.py database/train/attack/hand/attack_print_client001_session01_highdef_photo_controlled.mov test.avi

You can use many of the tweaking options defined in the batch processing scripts to fine tune the output behavior. Use --help to find-out more information about this program.