Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work explores the type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy, and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophic logic allows the assessment of many sources of ambiguity and conflicting information, decision-making is more flexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signature verification by demonstrating its superior handling of uncertainty and variability over type-1, which eventually results in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In a comparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similarity measure yields a better accuracy rate of 98% than the type-1 95%.
