Masked Face Images Based Gender Classification using Hybrid Bat Algorithm Optimized Bagging

Abstract

The face is one of the biometrics utilized to learn information from a person, such as gender. Gender classification study is expanding daily as a result of how important it is and how many other sectors, like forensics, security, business, and others, employ it. However, in order to protect themselves and stop the spread of Covid-19 during this epidemic, everyone must wear a face mask. Because many crucial facial features that help determine a person's gender are obscured by masks, using one creates an issue for the gender classification system. To obtain optimal performance outcomes, suitable hyperparameters are also required. As a result, the objective of this study is to develop a gender categorization system based on mask-covered faces utilizing a novel technique that combines several features in the Gray Level Co-occurrence Matrix (GLCM), which is then fed into the Bagging classifier.A Hybrid Bat Algorithm (HBA) is used to optimize the bagging hyperparameters. With 97% accuracy, precision, recall, and f1-score values, the suggested model is demonstrated to have greater performance than before the hyperparameters were tuned using HBA.