Elina van Kempen, Zane Karl, Richard Deamicis, Qi Alfred Chen (UC Irivine)

Biometric authentication systems, such as fingerprint scanning or facial recognition, are now commonplace and available on the majority of new smartphones and laptops. With the development of tablet-digital pen systems, the deployment of handwriting authentication is to be considered.

In this paper, we evaluate the viability of using the dynamic properties of handwriting, provided by the Apple Pencil, to distinguish and authenticate individuals. Following the data collection phase involving 30 participants, we examined the accuracy of time-series classification models on different inputs and on textindependent against text-dependent authentication, and we analyzed the effect of handwriting forgery. Additionally, participants completed a user survey to gather insight on the public reception of handwriting authentication. While classification models proved to have high accuracy, above 99% in many cases, and participants had a globally positive view of handwriting authentication, the models were not always robust against forgeries, with up to 21.3% forgery success rate. Overall, participants were positive about using handwriting authentication but showed some concern regarding its privacy and security impacts.

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