Autogenic, prognostic, and collective signature affirmation framework based on diverse set of features Online publication date: Fri, 01-Sep-2023
by Sameera Khan; Megha Mishra; Vishnu Kumar Mishra
International Journal of Biometrics (IJBM), Vol. 15, No. 5, 2023
Abstract: Use of forged signatures for fraudulent practices has become extremely common in recent days; therefore, a significant role is performed by the automatic signature verification (SV) process. Such verifiers need large number of specimens of a person's signature to establish the intrapersonal variability adequately. It is important to deal with the problem of data unavailability for training. A method to train with a single reference signature is proposed here to minimise the aforementioned limitation. This methodology is analysed by utilising a novel Gaussian gated recurrent unit neural network (2GRUNN) classifier. The single signature image is retrieved from database. Then, by using sinusoidal transformation, the signature duplication is performed. Next, pre-processing, feature extraction (FE), and feature selection (FS) are conducted. By employing linear chaotic shell game optimisation (LCSGO), the FS is executed. Extracted feature is fed to the proposed 2GRUNN for classification. Lastly, the results are compared with the existing methodologies.
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