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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 » TunnelDBSoftBio



INSTRUCTIONS FOR DOWNLOADING TunnelDBSoftBio

  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: TunnelDBSoftBio]

    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 TunnelDBSoftBio

The database is comprised of 13,340 soft biometric signals (from 23 physical trait labels) from 58 users annotated by 10 different annotators, as described in [TIFS2013].


    FILES FORMAT

The physical trait labels are stored in .mat (MATLAB) file. After loading each file, soft biometric signals corresponding to the 23 physical trait labels are stored respectively in one matrix (TunnelDBSoftBio) of dimensions 26 by 580, i.e., 3 rows of identificators and 23 rows of physical trait labels by 10 annotators from 58 subjects. Each column in the matrix represents a soft biometric signature from the 58 subjects.

As described in [TIFS2013] and [TPAMI2013], the database was annotated against recordings taken of the individuals in laboratory conditions. The annotation process was as follows: an annotator visualized the full video of a subject walking toward the camera and then generated one set of soft labels per each video. It is important to note that the process followed here is independent of the distance. A range of discrete values is given to each trait label, e.g. Arm length marked as 1 (very short), 2 (short), 3 (average), 4 (long), and 5 (very long).

    INDEX FILES

The nomenclature followed to identify the physical soft biometric label is as follows:

  • ROW 1 = sss: subject identifier

  • ROW 2 = xxxx: sample identifier

  • ROW 3 = aaa: annotator identifier

  • ROW 4...26 = soft labels based on the right table

    • ROW 4 = arm length [1...5]

    • ROW 5 = arm thickness [1...5]

    • ROW 6 = chest [1...5]

    • ROW 7 = figure [1...5]

    • ROW 8 = height [1...5]

    • ROW 9 = hips [1...5]

    • ROW 10 = leg length [1...5]

    • ROW 11 = leg direction [1...5]

    • ROW 12 = leg thickness [1...5]

    • ROW 13 = muscle build [1...5]

    • ROW 14 = proportions [1...2]

    • ROW 15 = shoulder shape [1...5]

    • ROW 16 = weight [1...5]

    • ROW 17 = age [1...7]

    • ROW 18 = ethnicity [1...7]

    • ROW 19 = sex [1...2]

    • ROW 20 = skin colour [1...4]

    • ROW 21 = facial hair colour [1...6]

    • ROW 22 = facial hair length [1...5]

    • ROW 23 = hair colour [1...6]

    • ROW 24 = hair length [1...5]

    • ROW 25 = neck length [1...5]

    • ROW 26 = neck thickness [1...5]




REFERENCES

For further information regarding the database, the different features approaches extracted from the data and the experimental work we refer the reader to:

Please remember to reference articles [TIFS2013] and [TPAMI2013] on any work made public, whatever the form, based directly or indirectly on any part of the TunnelDBSoftBio.

Data collected at CSPC - University of Southampton and processed at ATVS - Universidad Autonoma de Madrid.

CSPC - Comunication, Signal Processing and Control Research Group, Southampton University Southampton University
ATVS - Biometric Recognition Research Group Universidad Autonoma de Madrid
 
Southampton Tunnel

Southampton Tunnel image examples

Soft labels description

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