Deep Neural Network-Based Gender Identification for Surveillance Restroom Restriction System

Abstract

Everyone is generally aware that there is sex separation in restrooms. This emphasizes the significance of sex-based toilet restrictions. Student’s behavior and ability to learn decency will be impacted by a system that identifies a person’s sex. This study offers a detecting mechanism that distinguishes between males and females. To categorize both sexes in schools, sex identification utilizing image processing was developed. The implementation of the sex identification system used the YOLOv3 technology. The study’s conclusions state that the detection model used has an mAP value of 95.28 %. The implementation of the Sex Identification for Restroom Restrictions is successful since the chosen model is advised in sex identification for both males and females.