  Handwritten signature verification poses a formidable challenge in biometrics
and document authenticity. The objective is to ascertain the authenticity of a
provided handwritten signature, distinguishing between genuine and forged ones.
This issue has many applications in sectors such as finance, legal
documentation, and security. Currently, the field of computer vision and
machine learning has made significant progress in the domain of handwritten
signature verification. The outcomes, however, may be enhanced depending on the
acquired findings, the structure of the datasets, and the used models. Four
stages make up our suggested strategy. First, we collected a large dataset of
12600 images from 420 distinct individuals, and each individual has 30
signatures of a certain kind (All authors signatures are genuine). In the
subsequent stage, the best features from each image were extracted using a deep
learning model named MobileNetV2. During the feature selection step, three
selectors neighborhood component analysis (NCA), Chi2, and mutual info (MI)
were used to pull out 200, 300, 400, and 500 features, giving a total of 12
feature vectors. Finally, 12 results have been obtained by applying machine
learning techniques such as SVM with kernels (rbf, poly, and linear), KNN, DT,
Linear Discriminant Analysis, and Naive Bayes. Without employing feature
selection techniques, our suggested offline signature verification achieved a
classification accuracy of 91.3%, whereas using the NCA feature selection
approach with just 300 features it achieved a classification accuracy of 97.7%.
High classification accuracy was achieved using the designed and suggested
model, which also has the benefit of being a self-organized framework.
Consequently, using the optimum minimally chosen features, the proposed method
could identify the best model performance and result validation prediction
vectors.
