We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. e database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. e database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. e lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. e same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. e results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.
Antal et al. REF introduced the MOBISIG database that contains finger-drawn pseudo-signatures from 83 persons, captured on a capacitive touch-screen sensor embedded in a mobile device.
Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus
Mobile Information Systems
Mobile Information Systems
Computer Science