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Databases
Please note the license must be signed by a permanent faculty or research member from your university, and a scanned copy must be attached to the application.
Raw biometric data
  • MobileB2C_BehavePassDB: Mobile HCI data (keystroke, touch gestures, background sensors) for behavioral biometrics used in the MobileB2C ongoing competition.
  • DeepSignDB: Database comprises a total of 1526 users from four different popular databases.
  • MobileTouchDB: On-line character database performed by 217 users, using 94 different smartphone models in an unsupervised scenario.
  • KBOC_DB: Keystroke dataset of the KBOC competition with more than 300 users and 24 keystroke samples acquired during 4 sessions.
  • TouchDB: public benchmark to evaluate swipe biometrics extracted from human-device interaction with touch screens.
  • e-BioDigit Database: Handwritten numerical digits database acquired using a Samsung Galaxy Note 10.1 general purpose tablet for a total of 93 users. All samples were acquired using the finger touch as input.
  • BIOGIGA Database: The baseline corpus of BioGiga consists of synthetic images at 94 GHz (within millimetre waves) of the body of 50 individuals. The images are the result of simulations carried out on 3D-corporal models at two types of scenarios (outdoors, indoors) and with two kinds of imaging systems (passive and active). These corporal models were previously generated based on body measures taken from real subjects.
  • e-BioSign-DS1-Signature DB: Signature database acquired from 5 different COTS devices in total, considering both pen stylus and also the finger. Signatures were collected in two sessions for 65 subjects.
  • BiosecurID-SONOF DB: Two datasets containing real and synthetic on-line and off-line signatures for 132 users, with 16 genuine signatures and 12 skilled forgeries per user.
  • ATVS Keystroke DB: A dataset containing keystroking data of 63 users, including 12 genuine and 12 skilled impostor samples per user.
  • ATVS-SG_NOTE: Signature database captured using a Samsung Galaxy Note device with 25 users and 20 signatures per user.
  • xLongSignDB (Extended version of the ATVS On-Line Signature Long-Term database): The dataset comprises the on-line signature data of the 29 common users to the BiosecurID and the Biosecure databases.
  • ATVS-FakeIris Database (ATVS-FIr DB): A dataset containing 1,600 real and fake fingerprint images specifically thought to assess the vulnerability of iris-based recognition systems to direct attacks and to evaluate the performance of liveness detection methods. Fake samples were captured from high quality printed iris images.
  • Swansea Footstep Biometric Database (SFootBD): Footstep database containing pressure information over time for two 88-sensor arrays. It contains 9,990 stride footstep (right and left) signals from 127 persons specifically thought to assess the performance of footsteps as a biometric.
  • ATVS-SyntheticSignature Database (ATVS-SSig DB): Two datasets containing 17,500 highly-realistic fully-synthetic on-line signatures specifically thought to assess the performance of signature-based recognition systems, or for security evaluation purposes.
  • ATVS-FakeFingerprint Database (ATVS-FFp DB): Two datasets containing over 4,500 real and fake fingerprint images specifically thought to assess the vulnerability of fingerprint-based recognition systems to direct attacks and to evaluate the performance of liveness detection methods. Fake samples were captured from gummy fingers generated both with and without the cooperation of the user. Three different sensors were used to acquire the database: flat-optical, flat-capacitive, and sweeping-thermal.
  • ATVS-DooDB: Finger-drawn graphical password database: Doodles and pseudo-signatures; 100 users
  • FVC2006 Fingerprint Database (FVC2006): the database used in the Fingerprint Verification Competition 2006
  • MCYT Bimodal Biometric Database (MCYT-Signature-100): on-line signature; 100 users
  • MCYT Bimodal Biometric Database (MCYT-SignatureOff-75): off-line signature; 75 users
  • MCYT Bimodal Biometric Database (MCYT-Fingerprint-100): fingerprint; 100 users
  • GANDiffFace: source code for the synthesis of face images with realistic variations.
Processed biometric data
  • TouchDB_Benchmark: This code includes a public benchmark to evaluate swipe biometrics extracted from human-device interaction with touch screens.
  • LFW Soft Biometrics Database: This database contains a collection of soft biometrics extracted from the LFW database. We include two versions of the database: 1) manual annotations, and 2) automatic annotations using two COTS: Face++ and Microsoft API. The database also provides face recognition scores from the 10-folds from view 2 of the LFW database using features extracted from the VGG-16 pre-trained model.
  • BiosecurID-SGlobalLocalFeat DB: The BiosecurID-SGlobalLocalFeat DB contains two complementary feature datasets, extracted from the BiosecurID signature corpus: Global features containing a fixed-length representation of the on-line signatures, and Local functions containing a variable-length representation of the on-line signatures, based on time sequences.
  • Guardia Civil Database (GCDB_Features): A dataset from real forensic casework containing 268 latent fingerprint minutia templates and their corresponding 268 mated tenprint fingerprint minutia templates, including rare minutiae.
  • SCfaceDB Facial Landmarks Database (SCfaceDB Landmarks): A dataset containing 21 facial landmarks (from 4,160 face images) from 130 users manually annotated by a human operator. The dataset is especially suited to perform experiments related to facial region extraction and face recognition.
  • Tunnel Database Soft Biometric (TunnelDBSoftBio): A dataset containing soft biometric signals (from 23 physical trait labels) of 58 users manually annotated by 10 different annotators. The dataset is especially suited to perform experiments related to soft biometrics and face recognition.
  • MCYT-SCORES Database (MCYT-SCORES): Three datasets of bimodal matching scores (signatures and fingerprints from 75 subjects of the MCYT database), together with scalar fingerprint quality measures labelled by a human expert, both in MATLAB and ASCII format.


BiDA Lab - Biometrics and Data Pattern Analytics Research Group » Databases » DooDB




INSTRUCTIONS FOR DOWNLOADING DooDB

  1. Download license agreement, send by email one signed and scanned copy to atvsuam.es according to the instructions given in point 2.
     

  2. Send an email to atvsuam.es, as follows:
    Subject: [DATABASE download: DooDB]

    Body    : Your name, e-mail, telephone number, organization, postal mail, purpose for which you will use the database, time and date at which you sent the email with the signed license agreement.
     

  3. Once the email copy of the license agreement has been received at ATVS, you will receive an email with a username, a password, and a time slot to download the database.
     

  4. Download the database, for which you will need to provide the authentication information given in step 4. After you finish the download, please notify by email to atvsuam.es that you have successfully completed the transaction.
     

  5. For more information, please contact: atvsuam.es



DESCRIPTION OF DooDB

The DooDB database comprises two subcorpora, each one containing a different modality:

Subcorpus 1: Doodles. Participants were asked to draw with their fingertip a doodle on a handheld device touchscreen (see Fig. 1) that they would use as a graphical password on a regular basis for authentication (e.g. instead of the PIN code). There were no restrictions regarding duration or shape. In most cases, users invented their own doodle at the time of acquisition.

Subcorpus 2: Pseudo-signatures. Participants were also asked to draw with their fingertip a simplified version of their signature, which they would also use as a graphical password on a regular basis. This could be, for example, their initials or part of their signature flourish. The main difference between doodles and this modality is that in this case, the dynamic process to produce the drawing is in general composed of natural and well trained movements.

Acquisition was performed using an HTC Touch HD mobile phone. The device has a resistive touchscreen of 2x3.5 in (ca. 5x8.5 cm). The x and y coordinates of the fingertip position are sampled at discrete time values t at 100Hz when the user presses the screen. The coordinate values represent milli-inches, so x values range between [0,2000] (width) and y values between [0,3500] (height). The time interval between consecutive samples is also stored. Some examples of doodles and pseudo-signatures are shown in Fig.2. The acquisition process was divided in two sessions, separated by an average period of two weeks. The donors were asked to draw with their fingertip on the handset touch screen holding it in their own hand, simulating thus real operating conditions. They were allowed to practice their drawings until they felt comfortable with them. Forgeries have also been captured in this database. To perform forgeries, users had visual access to the doodle or pseudo-signature they had to imitate. The acquisition software replayed the strokes on the screen showing their dynamic properties (e.g. speed). This animation was shown to users up to three times, and then they were allowed to train until they felt confident with their forgery. The usage of the replay software makes possible to produce forgeries with a notable degree of accuracy, as can be observed in Fig.2. During the two sessions, the same protocol was followed for each user and modality: 5 genuine samples, then 5 forgeries, 5 genuine samples, followed by 5 forgeries and finally 5 genuine samples. This separation in blocks of 5 signatures allows analyzing intra-session variability. Consequently, at the end of the two sessions, each user had produced 30 genuine drawings (15 per session) and 20 forgeries. In the first session, user n produced forgeries for users n-1 and n-2, while in the second, forgeries for users n-3 and n-4 were produced.


On-line syntetic signatures 2

Figure 1. Doodle acquisition setup.


 On-line syntetic signatures 2

Figure 2. Examples of doodles (left) and pseudo-signatures (right).


FILES FORMAT

The doodles and pseudo-signatures are stored in text files following the following format:

  • COLUMN 1: represents the x coordinate.

  • COLUMN 2: represents the y coordinate.

  • COLUMN 3: this is a synthetic timestamp, representing the time interval in ms between samples.


FILES NOMENCLATURE

Users are numbered by the following pattern:

  • [2000,...,2099] for doodles

  • [1000,..., 1099] for pseudo-signatures. User 2XXX in the pseudo-signature dataset corresponds to user 1XXX in the doodle dataset.

For each user, there is a folder named SS1 for session 1 and SS2 for session 2.

The nomenclature followed to name the doodle and pseudo-signature files is as follows: SIGN_TTTB_USXXXX_USYYYY_ZZ.txt

  • TTT: is the doodle type; GEN for genuine doodles and FOR for forgeries

  • B: is the capture block within the session [1, 2, 3]

  • XXXX: is the number of the user performing the doodle[1001, 1002, ... , 1029]

  • YYYY: is the number of the owner of the doodle being traced [1001, 1002, ... , 1029]. This is the same user for genuine samples and the number of the user whose doodle is being forged in the case of forgeries.

  • ZZ: is the number of the sample within the capture session[1, 2, ... , 25]



REFERENCES

For further information on the database, we refer the reader to the open acces article:

Please remember to reference article [ACCESS2013] on any work made public, whatever the form, based directly or indirectly on any part of the DooDB database.
 
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