Signatures verification based on PNN classifier optimised by PSO algorithm

• We propose a new dynamic signature verification method based on composed features.
• Each feature is paired with a similarity measure and form a composed feature.
• Optimal set of composed features is determined in the Hotelling reduction process.
• Chosen composed features are used as input data for a classifier.
• PNN classifier parameters are tuned by means of the Particle Swarm Optimization (PSO) algorithm.

In this paper, we propose a new biometric pattern recognition method. In classical techniques only features of raw objects are compared. In our approach we will use composed signatures’ features. Features of a signature are associated with appropriate similarity coefficients and individually matched to a given signature. If it is necessary, composed features can be reduced. In the proposed study the most promising results are obtained from Hotelling's approach. Data comprising the composed features allow to achieve higher signature recognition level, compared to unprocessed (raw) data. It is the greatest novelty of the paper—the proposed method of data reduction together with a new type of similarity measure gives a high signature recognition level for various classes of classifiers.Leaning on investigations carried out, the classifier based on the Probabilistic Neural Network (PNN) has been introduced. Optimal parameters of the PNN have been determined by means of the Particle Swarm Optimization (PSO) procedure. The two class PNN classifier demonstrates high efficiency, compared to other classifiers. The described signature verification system consists of three units where features are captured, composed features are prepared, data are reduced and verified. The results of the study carried on signatures of the SVC2004 and MCYT databases and demonstrate the effectiveness of the proposed approach in comparison with other methods from the literature.