Analysis of the Effect of Different Features' Performance on Hidden Markov Modeling Based Online and Offline Signature Verification Systems

Shakil, Asma; Ahmad, Sharifah Mumtazah Syed; Anwar, Rabia; Balbed, Mustafa Agil Muhamad

doi:10.1109/dicta.2008.76

Cited by 6 publications

(2 citation statements)

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“…In the field of handwriting authentication and signature verification, intra-and inter-participant variations of axial pen pressure, spatiotemporal features and kinematic characteristics have been explored [2,[25][26][27][28][29]. Ramsay [2] modeled the dynamics of spatiotemporal information of handwriting using a differential equation and classified handwriting samples of different individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Lei and Govindaraju [26] examined the consistency and discriminative power of multiple features commonly used in on-line signature verification systems, concluding that the pen-tip coordinates, speed, and angle between the speed vector and the horizontal axis of the writing surface were among the most consistent features. Another study found that the dynamic features (speed, angle, axial pressure, and acceleration) surpassed static features in discriminative capability between genuine writing and skilled forgeries [27]. Bashir and Kempf [30,31] recently identified person-specific features in grip force signals and reported improved writer recognition when a grip force signal was added to the classifier.…”

Section: Introductionmentioning

confidence: 99%

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Grip Kinetic Profile Variability in Adult Signature Writing

Ghali¹,

Mamun²,

Chau³

2013

J Biomet Biostat

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Previous studies of handwriting grip kinetics have demonstrated the ability to classify writers based on the topography of grip forces associated with signature writing. However, the topographic representation requires a large array of individual sensors in practice. The possibility of differentiating participants on the basis of a summative, temporal force profile is yet unknown. In this study, we investigated the variability of features derived from a time-evolving total grip force profile. Using an instrumented writing utensil, twenty adult participants provided 600 samples of a well-practised bogus signature over a period of 10 days. Deploying a combination of temporal, spectral and information-theoretic features, a linear discriminant analysis classifier outperformed nonparametric and nonlinear classifier alternatives and discriminated among participants with an average misclassification rate of 5.8% as estimated by cross-validation. These results suggest the existence of a unique kinetic profile for each writer even when generating the same written product. Our findings highlight the potential of using grip kinetics as a biometric measure. Journal of Biometrics & Biostatistics Journa l o f B io m etrics & B io s ta tistics

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