Improving arabic signature authentication with quantum inspired evolutionary feature selection

Offline signature verification (OSV) and identification systems have been used to identify individuals. It has become a significant task to validate the authenticity of forensic and commercial transactions. The accuracy of signature authentication greatly depends on feature extraction and feature selection (FS). Regarding features selection, the less discriminatory features in the global and local features approach result in a reduced recognition rate. Therefore, stable and apropos features have a significant influence on signature verification. A genetic algorithm (GA) was used to select the best features, and it is an effective and efficient technique for optimization and machine learning applications. However, GA has a specific limitation when the dimension of the search increases, such as lack of extensive exploitation and, difficult obtaining good convergence. Thus, the computation increases exponentially, and the complexity of the solution increases. This work proposes a novel Quantum Inspired Genetic Algorithm (QIGA) FS approach based on GA and Quantum Rotation Computing (QRC) to increase the diversity of populations and overcome computational cost and early convergence problems. It employs a superposition in both mutation (divergence) and crossover (convergence) operations to enhance diversity and address population selection. Three public signature databases (SID Arabic handwriting signatures, CEDAR, and UTSIG) are employed to validate the proposed method. The experimental results show that the proposed model enhanced the performance of the classical GA by adopting quantum-inspired computing concepts.