Smartphones having sensors any one lesser than these four, were not used for data collection, in order to have consistent number of features under each sample. The sensors used to collect this data, present in the smartphone are the gyroscope, magnetometer, GPS, and accelerometer. The iOS and Android based smartphones (namely iPhone7 and Redmi Note 7) were used for the data collection. Each of the session were at least 15 days apart. A total of 30 samples towards the genuine signature over 3 sessions with 10 samples per session while 15 samples of the skilled forgery with 5 samples per session were collected.

The iSignDB consists of the genuine signature samples of a user as well as the skilled forgery samples where imposter was given multiple attempts to mimic the mannerisms of the original signer before giving skilled forgery samples. In the present work, we describe biometric signature dataset iSignDB captured using smartphone. However, there is scarcity of publicly available databases for the online signatures collected using smartphone. able to capture sensor logs and have been used widely in the literature to capture the dynamics of users’ behaviour while a signer signs on his smartphone. accelerometer, gyroscope, magnetometer, GPS, etc. In todays scenario, smartphones are widely used world-wide, and come equipped with various sensors e.g.

The offline verification considers only the static features of the signatures through the signature image, while the online verification considers various dynamic features associated with the signature such as pen pressure, pen tilt angle, velocity, acceleration, pen up and pen down, etc at various time stamps which are recorded using special digitizing tablets such as Wacom devices (STU-500, STU-530 and DTU-1031) etc. The signature verification can be offline or online. These signatures have user specific features that differentiate the individual for authentication. The signature has long been in use for the user verification.

Angular 10 signature pad