2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 18 , Issue 1 , PP: 130-144, 2025 | Cite this article as | XML | PDF | Full Length Article
Ibtisam Ghazi Nsaif 1 * , Sharifah Mumtazah Syed Ahmad 2 , Syamsiah Bt. Mashohor 3 , Marsyita Bt. Hanafi 4
Doi: https://doi.org/10.54216/FPA.180111
Biometrics has reached an important place in the field of authentication for both financial transactions and document verification. Signatures can be broadly classified into online and offline types, depending on how they are acquired. Captured through devices like tablets and digital pens, online signatures contain rich features concerning position, velocity, and acceleration; hence, they offer a better resistance to forgery compared to offline, more traditionally taken signatures. The review summarized the current research in online signature verification systems. There are methodologies and techniques deployed for feature extraction, data pre-processing, and classification. The main stages reviewed within the verification process are about data acquisition, including the use of several publicly available databases like DEEPSIGN, SVC2004 and MCYT-100. Wavelet transforms and Fourier analysis are discussed as a number of methods employed for feature extraction, showing good results about signature dynamics. This review follows the SLR approach for analysing and synthesizing relevant studies published between 2017 and 2024. This review uses PRISMA guidelines for the selection of studies, hence making the results methodologically rigorous and unbiased. The paper identifies commonly used algorithms, including CNN, RNN, and DTW, and examines popular signature databases by outlining their characteristics and relevance to system performance. The insights from this review will help in pointing towards the future ahead in online signature verification systems through emphasizing deep learning-based techniques along with realistic challenges.
Online signature , Deep learning , Public databases , Common algorithms
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