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Year : 2020  |  Volume : 10  |  Issue : 3  |  Page : 145-157

Online handwritten signature verification and recognition based on dual-tree complex wavelet packet transform

1 Department of Applied Mathematics, Faculty of Sciences and Modern Technology, Graduate University of Advanced Technology, Kerman, Iran
2 Department of Applied Mathematics, Faculty of Mathematics and Computer; Mahani Mathematical Research Center, Shahid Bahonar University of Kerman, Kerman, Iran
3 Department of Biomedical Engineering, School of Advanced Technologies in Medicine; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Correspondence Address:
Prof. Ataollah Askari Hemmat
Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmss.JMSS_44_19

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Background: With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication. Methods: In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT). Results: In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov–Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset. Conclusion: Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects.

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