A unified pattern recognition approach for low-frequency biometric signals

During the last decade many intelligent systems that help us in everyday life have come into being. Artificial intelligence is a widespread sphere among lots of people and scientific laboratories, as it includes different fields that are instantly developing nowadays. Pattern recognition is an important part of artificial intelligence: it helps to build secure information systems, technical devices, applications that use augmented reality techniques, etc. Biometric identification approaches apply pattern recognition methods for different types of signals in order to protect access to the systems' data. However, most of the existing methods consider only one type of biometric signals, as it is hard to combine characteristics for various signals; moreover, they are focused on obtaining the data from high frequency devices. This paper presents a new unified pattern recognition approach that can be used in order to identify various types of biometric signals based on their characteristics.