It's a database created to design foolproof biometric signature authentication system for smartphone users.

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• 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 licensed MathWorks cloud account and with Android and iOS smartphone devices e.g., 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 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.) with genuine signer signs on the touch screen of a smartphone.

• A total of 30 genuine and 15 fake samples were collected for every 32 users.

• One sign of a user contains a sensor log of these devices captured using sensors present in device iPhone 7 Plus: 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 only put biometric sign database of user 1 only in this repository, but as soon as this work is published, we will make the full database of 32 users available here with some terms and conditions.

• We successfully trained 32 BiLSTM models on dynamic signature dataset created with EER of 3.35% which is a significant improvement overall 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).

• Nomenclature for files in the dataset iSignDB: (each sign with 4 sensor logs corresponding to Acceleration, Angular Velocity, Magnetic Field, 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

• iSignDB will be made available to other researchers only after signing its "Term of use" agreement.

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