Commit 6d6dae2c authored by Philip ABBET's avatar Philip ABBET
Browse files

Add atvskeystroke/3 (api change: beat.backend.python v1.4.1)

parent 81fcf187
"description": "The ATVS Keystroke database",
"root_folder": "/idiap/group/biometric/databases/atvs_keystroke",
"protocols": [
"name": "A",
"template": "simple_keystroke_recognition",
"sets": [
"name": "templates",
"template": "templates",
"view": "Templates",
"parameters": {
"protocol": "A"
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/text/1",
"template_id": "{{ system_user.username }}/text/1",
"keystroke": "{{ user.username }}/atvs_keystroke/1"
"name": "probes",
"template": "probes",
"view": "Probes",
"parameters": {
"protocol": "A"
"outputs": {
"file_id": "{{ system_user.username }}/uint64/1",
"probe_id": "{{ system_user.username }}/uint64/1",
"client_id": "{{ system_user.username }}/text/1",
"template_ids": "{{ system_user.username }}/array_1d_text/1",
"keystroke": "{{ user.username }}/atvs_keystroke/1"
\ No newline at end of file
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The ATVS-Keystroke Database
* **Version 3**, 27/Oct/2017:
- Port to beat.backend.python v1.4.1
* **Version 2**, 26/Jan/2016:
- Port to Bob v2
* **Version 1**, 09/Jul/2015:
- Initial release
The `ATVS-Keystroke database <>`_
is a dataset captured for performance evaluation of Keystroke Dynamics
recognition systems (see [BTAS15]_ for all the details). The database
comprises 63 users with 12 genuine access and 12 impostor access for
each user for a total number of samples equal to 7680 (63 users x 24 access
x 5 data). There are people from two different nationalities with 60% of males
and 40% females. The acquisition was made in two sessions according a
semi-supervised protocol:
1. **First session**: the users were asked to introduce their personal data
in the platform. This process was repeated six times.
2. **Second session**: after at least 24 hours, the users were asked to
introduce once again their personal data in the platform. The process was
repeated six times. In addition, in this second session, each user acted as an
impostor trying to spoof the system with the personal data of another user.
The personal data of three other users was showed to each of the impostor and
they introduced them four times for a total number of impostor access of
twelve per user.
The information provided by the users includes sensitive data and therefore,
it has been post-processed to remove all the personal information (the
characters pressed) and to maintain the privacy of the users enrolled in the
database. The keystroke dynamic patterns were recorded using a key-logger
(programmed in Java). The key-logger detects two different types of events:
press and release. The timestamps for each of the detected events were
recorded in milliseconds.
For further information on the database we refer the reader to (the following
article is publicly available in the publications section of the
`ATVS group webpage <>`_ .)
.. [BTAS15] A. Morales, M. Falanga, J. Fierrez, C. Sansone and J. Ortega-Garcia, ''Keystroke Dynamics Recognition based on Personal Data: A Comparative Experimental Evaluation Implementing Reproducible Research'', in Proc. of the IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems, Arlington, Virginia, USA, September 2015.
Please remember to reference [BTAS15]_ on any work made public, whatever the
form, based directly or indirectly on any part of the ATVS-Keystroke DB.
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