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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 » xLongSignDB




INSTRUCTIONS FOR DOWNLOADING xLongSignDB

  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: xLongSignDB]

    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 xLongSignDB

The dataset comprises the on-line signature data of the 29 common users to the BiosecurID and the Biosecure databases. These two signature subsets were acquired in a 15 month time span and present some unique features that make them especially suited for aging evaluation of on-line signature recognition systems [IET2019]. The general time distribution of the different sessions of the database is shown in Fig. 1.


On-line syntetic signatures 2

Figure 1. General time diagram of the different acquisition sessions that form the xLongSignDB.



    The BiosecurID Signature Subset [PAA2010]

It comprises 16 original signatures per user (29 users).

Samples were captured in 4 separate acquisition sessions (named S1, S2, S3 and S4 in Fig. 1).

The sessions were captured leaving a two month interval between them, in a controlled and supervised office-like scenario.

Users were asked to sign on a piece of paper, inside a grid that marked the valid signing space, using an inking pen. The paper was placed on the Wacom Intuos 3 pen tablet that captured the time signals of each signature at a 100Hz sampling rate (trajectory functions x and y with an accuracy of 0.25mm, pressure function p with a precision of 1024 pressure levels, and azimuth and altitute angles). All the dynamic information is stored in separate text files following the format used in the first Signature Verification Competition, SVC.


    The Biosecure Signature Subset [PAMI2010]

This dataset was captured 6 months after the BiosecurID acquisition campaign had finished (the time sequence of the two databases is shown in Fig. 1).

It comprises 30 original signatures per user (same 29 users as the BiosecurID subset) distributed in two acquisition sessions separated three months (named S5 and S6 in Fig. 1).

The 15 original samples corresponding to each session were captured in three groups of 5 consecutive signatures with an interval of around 15 minutes between groups (named in Fig. 1 S.5.1-2-3 and S.6.1-2-3, respectively).

The signature dataset was designed to be fully compatible with the BiosecurID one. The acquisition scenario and protocol are almost identical: as in the BiosecurID case, users had to sign using an inking pen on a piece of paper with a restricted space, placed over the Wacom Intuos 3 pen tablet. The dynamic information stored is the same as in BiosecurID and following also the SVC format.


    FILES FORMAT

The signatures are stored in text files following the format of the 2004 Signature Verification Competition (SVC), where:

  • ROW 1: it just contains one entry with the number of sampled points of the signature (N).

  • ROWS 2 to (N+1): represents the y coordinate.

    • COLUMN 1: represents the x coordinate.

    • COLUMN 2: represents the y coordinate.

    • COLUMN 3: this is a synthetic timestamp (it may be neglected).

    • COLUMN 4: indicates the penups. It is 0 where p=0, and 1 otherwise.

    • COLUMN 5: this is the azimuth angle.

    • COLUMN 6: this is the altitude angle.

    • COLUMN 7: represents the pressure (p) function.


    FILES NOMENCLATURE

The nomenclature followed to name the signature files is as follows: XXXX_sgYY.svc

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

  • YY: is the number of the sample [1, 2, ... , 46]

The correspondence between signatures is as follows: XXXX_sgYY.svc

  • Signatures 1-4: S1 (1st session of BiosecurID)

  • Signatures 5-8: S2 (2nd session of BiosecurID)

  • Signatures 9-12: S3 (3rd session of BiosecurID)

  • Signatures 13-16: S4 (4th session of BiosecurID)

  • Signatures 17-31: S5 (1st session of Biosecure)

  • Signatures 32-46: S6 (2nd session of Biosecure)



REFERENCES

For further information on the database and on different applications where it has been used, we refer the reader to (all these articles are publicly available in the publications section of the ATVS group webpage.)

  • [IET2019] R. Tolosana, R. Vera-Rodriguez, J. Fierrez and J. Ortega-Garcia, “Reducing the Template Aging Effect in On-Line Signature Biometrics”, IET Biometrics, 2019.

  • [PONE2013] J.Galbally, M. Martinez-Diaz and Julian Fierrez, "Aging in Biometrics: An Experimental Analysis on On-Line Signature", PLOS ONE, Vol. 8, n. 7, 2013 (DOI).

  • [PAA2010] J. Fierrez, J. Galbally, J. Ortega-Garcia, M. R. Freire, F. Alonso-Fernandez, D. Ramos, D. T. Toledano, J. Gonzalez-Rodriguez, J. A. Siguenza, J. Garrido-Salas, E. Anguiano, G. Gonzalez-de-Rivera, R. Ribalda, M. Faundez-Zanuy, J. A. Ortega, V. Cardeñoso-Payo, A. Viloria, C. E. Vivaracho, Q. I. Moro, J. J. Igarza, J. Sanchez, I. Hernaez, C. Orrite-Uruñuela, F. Martinez-Contreras and J. J. Gracia-Roche, "BiosecurID: A Multimodal Biometric Database", Pattern Analysis and Applications, Vol. 13, n. 2, pp. 235-246, 2010.

  • [PAMI2010] J. Ortega-Garcia, J. Fierrez, F. Alonso-Fernandez, J. Galbally, M. Freire, J. Gonzalez-Rodriguez, C.Garcia-Mateo, J.-L.Alba-Castro, E.Gonzalez-Agulla, E.Otero-Muras, S.Garcia-Salicetti, L.Allano, B.Ly-Van, B.Dorizzi, J.Kittler, T.Bourlai, N.Poh, F.Deravi, M.Ng, M.Fairhurst, J.Hennebert, A.Humm, M.Tistarelli, L.Brodo, J.Richiardi, A.Drygajlo, H.Ganster, F.M.Sukno, S.-K.Pavani, A.Frangi, L.Akarun and A.Savran, "The Multi-Scenario Multi-Environment BioSecure Multimodal Database (BMDB)", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 32, n. 6, pp. 1097-1111, 2010.

Please remember to reference article [PONE2013] on any work made public, whatever the form, based directly or indirectly on any part of the ATVS-SSig DB.
 
Sample corresponding to BID1

Sample corresponding to BID2

Sample corresponding to BID3

Sample corresponding to BID4

Sample corresponding to Bure1

Sample corresponding to Bure2
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