In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates disproportions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.
The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.
Artificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper presents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to similar monitoring applications of other industrial dynamic objects.
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