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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 » e-BioDigit


INSTRUCTIONS FOR DOWNLOADING e-BioDigitDB

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

  2. Send an email to atvs@uam.es, as follows:
    Subject: [DATABASE download: e-BioDigitDB]

    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 atvs@uam.es that you have successfully completed the transaction.
     

  5. For more information, please contact: atvs@uam.es



DESCRIPTION OF e-BioDigitDB

The e-BioDigit database comprises on-line handwritten numerical digits from 0 to 9 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 so only information related to X and Y spatial coordinates are considered. Regarding the acquisition protocol, data subjects had to perform handwritten numerical digits from 0 to 9 one at a time. Some examples of the handwritten numerical digits acquired for the e-BioDigit database are depicted in Figure 1. Additionally, samples were collected in two sessions with a time gap of at least three weeks between them in order to consider inter-session variability, very important for behavioural biometric traits. For each session, users had to perform a total of 4 numerical sequences from 0 to 9. Therefore, there are a total of 8 samples per numerical digit and user. The software for capturing handwritten numerical digits was developed in order to minimize the variability of the user during the acquisition process. A rectangular area with a writing surface size similar to a 5-inch screen smartphone was considered . A horizontal line was represented in the bottom part of the rectangular area, including two buttons OK and Cancel to press after writing if the sample was good or bad respectively. If the sample was not good, then it was repeated.


Digit 0 Digit 0 Digit 0

Figure 1. Examples of different handwritten numerical digits of the e-BioDigit database. X and Y denote horizontal and vertical position versus the time samples. [CVPR2018_OTP].




    FILES FORMAT

The handwritten numerical digits are stored in text files, where:

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

  • ROWS 2 to (N+1):

    • COLUMN 1: represents the X coordinate.

    • COLUMN 2: represents the Y coordinate.

    • COLUMN 3: represents the timestamp.

    • COLUMN 4: represents the pressure function. All values were set to 255 as this information was not available when using the finger touch.


    FILES NOMENCLATURE

The nomenclature followed to name the numerical digit files is as follows: uAAA_digit_B_CCC.txt

  • AAA: indicates the number of the user [101, 102, ... , 208]. Some users in between were finally removed having in total 93 users.

  • B: indicates the numerical digit [0, 1, ... , 9].

  • CCC: indicates the number of the acquisition sample [002, 004, ... , 016]. There are a total of 8 samples per numerical digit and user.

Finally, handwritten numerical digits are organized into "session_1" and "session_2" folders.



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 BiDA group webpage.)

  • [TMC2019_OTP] R. Tolosana, R. Vera-Rodriguez, and J. Fierrez, "BioTouchPass: Handwritten Passwords for Touchscreen Biometrics", IEEE Transactions on Mobile Computing, 2019.

  • [CVPR2018_OTP] R. Tolosana, R. Vera-Rodriguez, J. Fierrez and J. Ortega-Garcia, "Incorporating Touch Biometrics to Mobile One-Time Passwords: Exploration of Digits.", in Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, CVPRW, Salt Lake City, USA, 2018..

Please remember to reference articles [TMC2019_OTP, CVPR2018_OTP] on any work made public, whatever the form, based directly or indirectly on any part of the e-BioDigitDB database.



ACKNOWLEDGMENTS

  • The acquisition of this database has been supported by projects: COGNIMETRICS (TEC2015-70627-R MINECO/FEDER), BIBECA (RTI2018- 101248-B-I00 MINECO/FEDER), Bio-Guard (Ayudas Fundaci�n BBVA a Equipos de Investigaci�n Cient�fica 2017) and Cecabank. The work of R. Tolosana was supported by a FPU Fellowship from Spanish MECD.

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