Commit b514bfa2 authored by Philip ABBET's avatar Philip ABBET

Add cpqd/3 (api change: beat.backend.python v1.4.2)

parent 997645b6
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.. Copyright (c) 2017 Idiap Research Institute, http://www.idiap.ch/ ..
.. Contact: beat.support@idiap.ch ..
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.. This file is part of the beat.examples module of the BEAT platform. ..
.. ..
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.. and Idiap. For further information contact tto@idiap.ch ..
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.. Alternatively, this file may be used under the terms of the GNU Affero ..
.. Public License version 3 as published by the Free Software and appearing ..
.. in the file LICENSE.AGPL included in the packaging of this file. ..
.. The BEAT platform is distributed in the hope that it will be useful, but ..
.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
.. or FITNESS FOR A PARTICULAR PURPOSE. ..
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.. You should have received a copy of the GNU Affero Public License along ..
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CPqD Biometric Database (BioCPqD Phase 1)
-----------------------------------------
Changelog
=========
* **Version 3**, 30/Oct/2017:
- Port to beat.backend.python v1.4.2
* **Version 2**, 20/Jan/2016:
- Port to Bob v2
* **Version 1**, 01/Apr/2014:
- Initial release
Description
===========
This database was designed to provide data that was recorded in a natural way,
using various devices in different environments. Hence, algorithms that
perform well on this database are expected to be suitable for other real-world
applications that do not require a predefined audio/video recording setup.
Database participants were selected among employees of CPqD Foundation who
volunteered to make recordings. A unique ID was assigned for each participant,
composed by a prefix (M for male and F for female) followed by a 4-digit number
(odd for males and even for females). Each participant recorded up to five
sessions, with a time lapse of at least 10 days between sessions.
Sessions consisted of 27 recorded sentences, whose content was specified in a
script. Each sentence was recorded on three different devices types:
- Laptops (audio and video content);
- Smartphones (audio and video content);
- Phone calls (only audio).
For each device type, a set of devices was used, as specified below:
- Laptops:
- Compaq 510 with embedded mic and camera;
- Toshiba with USB Logitech QuickCam Pro 9000 webcam;
- DELL Latitude embedded mic and camera.
- Smartphones:
- Samsung Galaxy S II;
- Apple iPhone 4;
- Apple iPhone 4.
- Phone calls:
- landline phone call;
- personal mobile phone call.
Recordings were made in three environments with different characteristics:
garden, restaurant (public indoor) and office. The idea behind this strategy
was to exploit the influence of environmental noise in audio recordings and the
effect of illumination and background conditions in the video recordings.
Since the database includes recordings captured on different devices of
different types and in different environments, it allows a large number of
experimental setups.
Content
=======
The data collection followed a simple recording protocol that was replicated
for all sessions. For each session there was a corresponding script describing
the whole content to be recorded, as follows:
Text reading:
- a pre-defined text (extracted from the database's consent form);
- four phonetically rich sentences (randomly selected among 562 options);
- passphrase: three repetitions a single sentence (the same sentence for all
participants in all sessions).
Spontaneous speech:
- answers for generic questions (all participants answered all 15 questions
selected form a fixed set, distributed along the 5 sessions in random order);
- a fake name;
- a fake address;
- a fake birthday date;
- a fake ID number;
- a fake phone number;
- two command words (all participants spoke 10 words along the 5 sessions in
random order).
Numbers, digits, time values and alphanumeric strings:
- a monetary amount between 10 and 10 000, randomly generated;
- a number between 10 and 1000, randomly generated;
- a number between 1000 and 10 million, randomly generated;
- three repetitions of a random digit sequence (first one read in a slow pace
and others naturally read);
- a fake credit card number;
- an alphanumeric string composed of 6 characters, randomly generated;
- a time value, selected among a predefined set with 181 samples, equally
distributed among participants.
It is important to note that all content was recorded in Brazilian Portuguese
language.
BioCPqD Phase I database provides unbiased biometric verification protocols,
one for male and one for female participants, based on the MOBIO database
protocols. These protocols partition the database in three different groups:
- a Training set: used to train the parameters of algorithm to be tested, e.g.,
to create the projection matrix, Universal Background Models, etc.;
- a Development set: used to evaluate hyper-parameters of the tested algorithms;
- a Test set: used to evaluate the generalization performance of the tested
algorithms with previously unseen data.
Both development and test sets are further split into an enrollment subset
(used to enroll participants' models), and a probe set (whose files will be
tested against all participants' models).
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