Online Signature Verification on Mobile Devices

Sae-Bae, Napa; Memon, Nasir

doi:10.1109/tifs.2014.2316472

Cited by 190 publications

(112 citation statements)

References 31 publications

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“…We have only found six studies [8][9][10][11][12][13] reporting results obtained on signature databases captured in mobile context. e properties of the databases used in these studies are presented in Table 1.…”

Section: Signature Recognition On Mobile Devicesmentioning

confidence: 99%

“…Sae-Bae and Memon [10] collected an online signature dataset from 180 users using HTML5. Users were allowed to enter their signatures using their own iOS devices.…”

Section: Signature Recognition On Mobile Devicesmentioning

confidence: 99%

“…In the computation of histogram-based features, we followed the work of Sae-Bae and Memon [10] except that we used fewer features.…”

Section: Feature-based Verificationmentioning

confidence: 99%

“…, sequence captures the speed distribution, and it is considered very useful in combating skilled forgeries [10]. In the histogram computation, we considered only the values from the interval [0, μ + 3σ], where μ represents the mean and σ the standard deviation of the r sequence obtained from a single signature.…”

Section: Feature-based Verificationmentioning

confidence: 99%

“…A number of researches have already been conducted in this topic, although the signature databases are not publicly available [8][9][10], except for DooDB database [11]. Compared to the DooDB database, which was collected on a device using a resistive touchscreen, our database was collected on a device with a capacitive touchscreen.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

Antal

Szabó

Tordai

2018

Mobile Information Systems

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We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. e database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. e database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. e lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. e same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. e results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.

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Section: Signature Recognition On Mobile Devicesmentioning

confidence: 99%

“…Sae-Bae and Memon [10] collected an online signature dataset from 180 users using HTML5. Users were allowed to enter their signatures using their own iOS devices.…”

Section: Signature Recognition On Mobile Devicesmentioning

confidence: 99%

“…In the computation of histogram-based features, we followed the work of Sae-Bae and Memon [10] except that we used fewer features.…”

Section: Feature-based Verificationmentioning

confidence: 99%

Section: Feature-based Verificationmentioning

confidence: 99%