Abstract
In this paper, a novel Multiple Distances Based Dynamic Time Warping (MDB-DTW) method is proposed for signature verification. In order to obtain more discriminative and complementary information, we take accounts of the multiple distance measurements on the Euclidian distance based DTW path. In addition, two classifiers (SVM-based classifier and PCA-based classifier) are adopted to fuse the useful information and remove the noise from the multiple dissimilarity vector space. The comprehensive experiments have conducted on three publicly accessible datasets MCYT-100, SUSIG and SVC-task2 with the obtained EER results are 1.87%, 1.28% and 6.32% respectively, which further demonstrates the robust and effectiveness of our proposed MDB-DTW method.
X. Lu and Y. Fang—Contributed equally to this paper.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61573151 and 61503141), the Guangdong Natural Science Foundation (No. 2016A030313468), Science and Technology Planning Project of Guangdong Province (No. 2017A010101026).
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Lu, X., Fang, Y., Wu, Q., Zhao, J., Kang, W. (2018). A Novel Multiple Distances Based Dynamic Time Warping Method for Online Signature Verification. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_68
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