iSignDB: A biometric signature database created using smartphone

iSignDB: A biometric signature database created using smartphoneSuraiya Jabin, Sumaiya Ahmad, Sarthak Mishra, and Farhana Javed ZareenDepartment of Computer Science, Jamia Millia Islamia, New Delhi-110025, IndiaIt'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 withtwodevicesiPhone 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 behaviourism (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.Currently, we put biometric sign database of one user only in this repository, but as soon as this work is published, we will make the full database of 32 users available with terms and conditions.We successfully trained 32 BiLSTM models on dynamic signature dataset created with EER of 3.35% which is a significant improvement over all such models in existing literature (HMOG, and eBioSignDS 2).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 will be made available to other researchers only after signing its "Term of use" agreement.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