Age and gender detection with real time database

Abstract

The research presented here uses cutting-edge technological components to propose a reliable and effective method for real-time detection. The approach uses CNN and a Caffe model that has already been trained to successfully classify age and gender attributes from. Furthermore, Google Firebase is used as the database backbone to provide real-time data management. The Open CV library is integrated into the system to make image processing and analysis easier. When compared to existing methods, the implementation of this algorithm exhibits excellent performance in age and gender classification. Real-time decision-making is made possible by the effective feature extraction and classification provided by CNN in conjunction with pre-trained models. Additionally, the system's scalability and real-time capabilities are improved by the integration with Google Firebase, making it appropriate for a variety of applications, such as surveillance, marketing, and customized user experiences. This study offers a useful solution for age and gender detection with real-time database capabilities, making a significant addition to the fields of computer vision and data analytics.