One of the most reliable methods of identity verification are biometric authentication methods. There are two types of methods: static and dynamic. Static methods include fingerprint scanning, 3D facial recognition, vein patterns, retina scanning, etc. Dynamic methods include voice verification, keyboard handwriting and signature recognition. As of today, static methods have the lowest type I and II error rates, because their primary principle of operation is based on capturing a person's biometric characteristics, which do not change throughout their lifetime. Unfortunately, this advantage, which accounts for such low type I and II error rates, is also a drawback when implementing this method for widespread use among internet services. If biometric data is compromised, user can no longer safely use method everywhere. Dynamic biometric authentication methods are based on a person's behavioral characteristics, allowing user to control information entered for authentication. However, behavioral characteristics are more vulnerable to changes than static, resulting in significantly different type I and II errors. The aim of this work is to analyze one of the dynamic methods of biometric authentication, which can be used in most internal and external information systems as a tool for authorization or confirmation of user intentions. Biometric user authentication based on their handwritten signature relies on comparing unique biometric features that can be extracted from signature image. These unique features are divided into two categories: static and dynamic. Static features are extracted from signature image, based on characteristics such as point coordinates, total length, and width of the signature. Dynamic features are based on coordinate dependency of the signature points over time. More unique features are identified and more accurately each is weighted, the better type I and II error rates will be. This work focuses on algorithms that extract unique features from static characteristics of signature, as most signature peculiarities are identified from the dependencies of writing individual segments of the signature image.

Keywords: static algorithms, metrics, signature length, scaling, signature angle

Currently, to access information contained in autonomous and external information systems, user must pass an authorization process using modern methods of identity verification, such as: password protection, protection based on one-time codes, electronic signature-based protection, etc. These methods as always have worked well and still continue to provide secure access, however, biometric authentication methods are more reliable when access to confidential information should be limited to a single user. Today, there are two types of biometric authentication methods: static and dynamic. Static methods based on a person's biological characteristics that remain with them throughout their life, while dynamic methods based on a person's behavioral characteristics. Static methods are considered some of the most accurate, because most biometric parameters do not change over a lifetime. However, this method should only be used if chance of data compromise is very low, because in the event of leak, user will not be able to continue using these types of methods anywhere else. Dynamic methods, due to their behavioral characteristics, do not have sufficiently satisfactory type I and II error rates, as they directly depend on user's psychological and physical state. However, unlike static methods, user can control the information that will serve as a secret key for authorization in the future, so in case of a leak, user can always change the contents of the key for current and future services. This work examines one of these dynamic methods of biometric authentication: verification by handwritten signature. This method is considered more attractive among its counterparts, as in case of successful type I and II error rates, it can be applied in most existing services as a tool for authentication and confirmation of user intentions when signing various types of documents. The article discusses the main algorithms for verifying handwritten signatures by identifying unique dynamic features, dependent on the temporal and coordinate values of the analyzed samples of handwritten signatures.

Keywords: dynamic algorithms, feature extraction, signature writing time, proximity of point coordinate functions, Fourier transform