iSignDB: A biometric signature database created using smartphone

iSignDB: A biometric signature database created using smartphone

Suraiya Jabin, Sumaiya Ahmad, Sarthak Mishra, and Farhana Javed Zareen

Department of Computer Science, Jamia Millia Islamia, New Delhi-110025, India

It's a database of biometric signatures recorded using sensors present in a smartphone. ​The dataset iSignDB is created to implement a novel anti-spoof biometric signature authentication for smartphone users.

• We named it iSignDB as we collected it using a licensed MathWorks cloud account and with iOS and Android based smartphone devices (iPhone 7 Plus and Redmi Note 7) for capturing dynamic signatures.
• A total of 48 subjects volunteered for data collection out of which we identified 32 users as genuine signature contributors and 16 users as fake signature contributors with skilled forgery.
• Data was collected in 3 different sessions separated by at least 20 days in order to capture the emotional intelligence of users.
• During each session, one pair of subjects, out of which one subject contributed 10 original signatures and the other contributed 5 fake signatures.
• For obtaining a fake signature, a subject was allowed to practice copying not only the signature image of a genuine user but also the behaviorism (e.g. number of touchpoints, style of finger movement while signing, etc.) while genuine signer signs on the touch screen of a smartphone.
• A total of 30 genuine and 15 fake samples were collected for each of 32 users.
• One sign of a user contains a sensor log captured using sensors present in the smartphone: Accelerometer, Gyroscope, Magnetometer, and GPS, etc along with images of signature as obtained by performing a sign on the touch screen of the device.
• We have uploaded the smartphone biometric sign database of all 32 users in this repository. The full database of 32 users is available for other researchers only after they sign and submit the terms and conditions of using it.
• We successfully trained 32 BiGRU models on dynamic signature dataset created with EER of 0.66% which is a significant improvement.
• We provide Matlab code (compatible with MATLAB 2020a licensed version) for training, testing, and calculating EER in this repository (https://github.com/suraiyajabin/iSignDB2020).
• iSignDB is available to other researchers only after signing its "Term of use" agreement and acknowledging it properly in their work.
• Nomenclature for files in the dataset iSignDB: (each sign with 5 sensor logs corresponding to Acceleration, Angular Velocity, Magnetic Field, Orientation, and Position, and image of signature

u01_s3_r010_AngVel.txt : means a signature of user 1, on session 3, real signature, 10th sample’s Angular velocity sensor log

u01_s1_f02_MagField.txt : means a signature of user 1, on session 1, fake signature, 2nd sample’s magnetic field sensor log

u01_s1_r01_im.png : image of the genuine signature of user 1, captured on session 1, sample 1