Intelligent Approach to the Prediction of Changes in Biometric Attributes

Abstract

Artificial intelligence methods are used in many fields of applications. One of them is biometrics. It deals with a broadly understood analysis of physical and behavioral biometric characteristics. Behavioral characteristics are particularly important from a practical point of view. They describe, among others, learned behaviors of human beings. They include the ways of signing, lip movement, gait, etc. Such features are difficult to forge, so their use in biometric systems increases security. The dynamics of signing is particularly important because it is considered socially acceptable worldwide. However, the problem associated with its processing results from its possible instability over time, as the signature is likely to evolve. In a situation where the method of signing is only partially altered (and not completely changed), the trend in possible changes can be predicted. In this article, we propose a new approach to predicting such changes. For this purpose, we use the possibilities offered by fuzzy systems (FSs) and population-based algorithms (PBAs). The purpose of applying the PBA is to select parameters and the structure of the FSs and to select reference signatures for each user separately so as to maximize prediction accuracy. Prediction results are included in the final verification of signatures. An important feature of the prediction mechanism is also its ability to interpret the changes occurring in signature handwriting because they are represented by the fuzzy rules base. This type of a flexible and nature-inspired prediction mechanism would be very difficult to implement with the use of other methods. The developed mechanisms can be used in various fields of applications—also in those not related to biometrics.