VERIFICATION OF STATIC SIGNATURE USING CONVOLUTIONAL NEURAL NETWORK
Keywords:
Recognition, verification, handwritten signature, classification, False Rejection Rate (FRR), False Acceptance Rate (FAR).Abstract
This article is devoted to the development of a method that provides verification of handwritten signatures based on real samples obtained by scanning with a resolution of 800 dpi. Handwritten signature remains one of the most common identification methods and consideration of the problems of this promising area contributes to the search for a solution to this problem
One of the main stages of recognition is classification. This article describes the results of handwritten signature recognition using a convolutional neural network. A database of handwritten signatures of 10 people was used for experiments. The signatures are digitized as color images with a resolution of 850×550 pixels. There are 10 genuine and 10 fake signatures for each person. Experiments were carried out with the reduction of signatures to the size 128×128, 256×256, 512×512 pixels.
As a result of the study of this model, it has shown its effectiveness and practical suitability for use in biometric identification systems.
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