On-line signature verification,

By Anil K. Jain, F. Griess, S. D. Connell. 2002

605 citations recorded by the source corpus.

Abstract We describe a method for on-line handwritten signature verification. The signatures are acquired using a digitizing tablet which captures both dynamic and spatial information of the writing. After preprocessing the signature, several features are extracted. The authenticity of a writer is determined by comparing an input signature to a stored reference set (template) consisting of three signatures. The similarity between an input signature and the reference set is computed using string matching and the similarity value is compared to a threshold. Several approaches for obtaining the optimal threshold value from the reference set are investigated. The best result yields a false reject rate of 2.8% and a false accept rate of 1.6%. Experiments on a database containing a total of 1232 signatures of 102 individuals show that writer-dependent thresholds yield better results than using a common threshold.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

SVC2004: First International Signature Verification Competition (2004, background): “Handwritten signature verification is the process of confirming the identity of a user based on the handwritten signature of the user as a form of behavioral biometrics [1–3].”
Automatic Signature Verification: The State of the Art (2008, methodology): “ical morphology [115], and Gaussian functions [37], [138], [180] have been used.”
'Online recognition of Chinese characters: the state-of-the-art (2004, background): “Signature verification has been reviewed in [61], [102] and recent works are reported in [44], [57].”
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication (2012, methodology): “Our contribution is a classiﬁcation framework that serves as a proof-of-concept for touch-based behavioral biometric authentication.”
An Off-line Signature Verification System Based on Fusion of Local and Global Information (2004, background): “In case of considering random forgeries (i.e., impostors are claiming others’ identities but are using their own signatures), client similarity scores are as above and the number of impostor similarity scores is 75 × 74 × 5 = 27750.”
HMM-based on-line signature verification: Feature extraction and signature modeling (2007, background): “User-specific decision thresholds produce results that in average usually outperform the global user-independent decision approach (Jain et al., 2002; Ortega-Garcia et al., 2003a).”