M. Muzaffar Hameed1,2, Rodina Ahmad1,*, Laiha Mat Kiah1, Ghulam Murtaza3, Noman Mazhar1
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1267-1289, 2023, DOI:10.32604/cmc.2023.035063
Abstract Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This… More >
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