Signature recognition is one of the most widespread and legally accepted methodology to authenticate a person's identity. In this work, we show how a haptic device can be used to acquire in-air 3D signatures, and provide the time-dependent position and orientation characteristics needed to effectively perform user verification. Dynamic time warping and hidden Markov models are here employed to compare samples acquired during the enrolment and verification stages. The recognition performance achieved when testing the proposed system on samples captured from 52 subjects testify the effectiveness of the proposed approach. Furthermore, a longitudinal analysis carried out on data from a subset of 21 subjects, for which two recording sessions have been taken at an average distance of four months, demonstrates that effective recognition can be performed even at long time distances from the enrolment.
Luisa, L.D., Hine, G.E., Maiorana, E., Campisi, P. (2021). In-Air 3D Dynamic Signature Recognition using Haptic Devices. In Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/IWBF50991.2021.9465089].
In-Air 3D Dynamic Signature Recognition using Haptic Devices
Hine G. E.;Maiorana E.;Campisi P.
2021-01-01
Abstract
Signature recognition is one of the most widespread and legally accepted methodology to authenticate a person's identity. In this work, we show how a haptic device can be used to acquire in-air 3D signatures, and provide the time-dependent position and orientation characteristics needed to effectively perform user verification. Dynamic time warping and hidden Markov models are here employed to compare samples acquired during the enrolment and verification stages. The recognition performance achieved when testing the proposed system on samples captured from 52 subjects testify the effectiveness of the proposed approach. Furthermore, a longitudinal analysis carried out on data from a subset of 21 subjects, for which two recording sessions have been taken at an average distance of four months, demonstrates that effective recognition can be performed even at long time distances from the enrolment.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
