Privacy-Preserving Biometric Verification with Handwritten Random Digit String
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Handwriting verification has stood as a steadfast identity authentication
method for decades. However, this technique risks potential privacy breaches
due to the inclusion of personal information in handwritten biometrics such as
signatures. To address this concern, we propose using the Random Digit String
(RDS) for privacy-preserving handwriting verification. This approach allows
users to authenticate themselves by writing an arbitrary digit sequence,
effectively ensuring privacy protection. To evaluate the effectiveness of RDS,
we construct a new HRDS4BV dataset composed of online naturally handwritten
RDS. Unlike conventional handwriting, RDS encompasses unconstrained and
variable content, posing significant challenges for modeling consistent
personal writing style. To surmount this, we propose the Pattern Attentive
VErification Network (PAVENet), along with a Discriminative Pattern Mining
(DPM) module. DPM adaptively enhances the recognition of consistent and
discriminative writing patterns, thus refining handwriting style
representation. Through comprehensive evaluations, we scrutinize the
applicability of online RDS verification and showcase a pronounced
outperformance of our model over existing methods. Furthermore, we discover a
noteworthy forgery phenomenon that deviates from prior findings and discuss its
positive impact in countering malicious impostor attacks. Substantially, our
work underscores the feasibility of privacy-preserving biometric verification
and propels the prospects of its broader acceptance and application.
