Signature recognition is identifying the signature’s owner, whereas verification is the process to find whether a signature is genuine or forged. Though, both are important in the field of forensic sciences, however, verification is more important to banks and credit card companies. In this paper, we have proposed a methodology to analyze 3D signatures captured using Leap motion sensor. We have extended existing 2D features into 3D from raw signatures and applied well-known classifiers for recognition as well as verification. We have shown that the 3rd dimension, which essentially represents instantaneous pressure during writing, can improve the accuracy of the biometric systems. We have created a large dataset containing more than 2000 signatures registered by 100 volunteers using the Leap motion interface. This has been made available online for the research community. Our analysis shows that, the proposed 3D extension is better than its original 2D version. Recognition and verification accuracy have increased by 6.8% and 9.5%, respectively using k-NN. Similarly, accuracy has increased by 9.9% (recognition) and 6.5% (verification) when HMM is used as the classifier. Similar results have been recorded on benchmark datasets. A comparison with 2D tablet-stylus interface has been carried out which also supports our claims. We believe, Leap motion can be an alternative to the existing biometric setups.
Multimedia Tools and Applications – Springer Journals
Published: Jul 21, 2017
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