Analysis of psychological biometric data for gender identification using fusion features and deep learning

Abstract

A person's gender must be correctly identified for many applications, such as tailored services, social sciences, and human-computer interaction, to function properly. Traditional gender identification techniques mostly depend on physical traits, but new developments in psychological biometrics provide interesting alternatives. Over the last several years, several access control systems have included biometric security technologies to increase security. The handwritten signature is the psychological biometric characteristic that is most often used to validate daily documents like letters, contracts, wills, MOUs, etc. This study proposes an innovative Deep Learning (DL) approach to identify a person's gender from an image of their handwritten signature. The fusion of statistical and textural information taken from the trademark photos serves as the foundation for the proposed work. The texture is represented by the Pyramid Histogram of Oriented Gradients (PHOG) features. A novel sequence labelling multidimensional recurrent neural network (SLMRNN) is employed to classify the writer's gender. Extensive experiments are carried out on the gathered dataset to assess the performance of the suggested technique. The efficacy of the fusion features and DL models for gender recognition is evaluated using a variety of measures, including accuracy, precision, recall, and F1 score. To evaluate the suggested approach against existing procedures and emphasize its advantages, if any, comparative assessments are also carried out. The findings show that, in comparison to conventional approaches, the combination of behavioral biometric variables with cutting-edge DL algorithms greatly enhances gender identification accuracy. The suggested approach is anticipated to be beneficial in the development of effective computer vision tools for forensic analysis and authentication of papers with handwritten signatures.