Feature selection method based on quantum inspired genetic algorithm for Arabic signature verification

The signature is a behavioral-based human characteristics that is extensively used as legal evidence of identification on bank checks, credit cards, and wills. Developing a good offline signature verification (OSV) system is essential to avoid fraud. Due to the plurality of signature styles and sample contexts, feature extraction (FE) is challenging for OSV systems. Current signature verification methods present promising results in distinguishing between genuine and forged signatures, but they are far from satisfactory. Regarding feature selection (FS), stable and appropriate features substantially impact signature verification. The issue with current approaches is that they apply combination features without addressing correlation and discriminant. A genetic algorithm (GA) is popularly deployed as a method for optimization to identify the best features. Regrettably, GA has drawbacks when the search dimension is expanded, such as a lack of comprehensive exploitation, difficulties in achieving good convergence, and therefore increased computation. This study addresses the aforementioned challenges by introducing multifeature fusion and discriminant FS using a novel quantum inspired GA (QIGA). QIGAs are constructed using quantum computing concepts such as qubits (Q-bits) and superposition of states. QIGA is able to express a linear superposition of solutions with the objective of increasing gene diversity by utilizing the qubit as a representation. Quantum rotation computing (QRC) enhances population diversity and tackles computing complexity and early convergence issues in GA. The superposition concept in QRC utilizes both mutation (divergence) and crossover (convergence) procedures to expand diversity and improve population selection. QIGA performs better in a parallel structure because of its quick convergence and robust global search capabilities. This implies that QIGA embodies features of both exploration and exploitation. The studied approach is applied to three different databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Consequently, the proposed OSV based on the QIGA improved equal error rate (EER) between 10% and 20% over the GA without influencing computation complexity.