Deep Learning Approaches for Gender Classification from Facial Images

Abstract

Gender recognition on the facial level is considered one of the most important technologies that finds use in such fields as a personalized marketing plan, safe systems of authentication, and effective human-computer interfaces. However, it has the following challenges; variation of lighting, facial movement, and ethnic/age face images. AI and DL has been improving on the effectiveness, flexibility, and speed of the gender classification system. AI enables complex and automatic feature learning in Data, while DL is tailored for handle variants in vision-based data. In this paper, we evaluated several architectures including Efficient Net_B2, ResNet50, ResNet18, and Lightning whilst determining the performance of the architectures in gender classification tasks. Self-assessment criteria included accuracy, precision, recall, and the F1-score. As for the performance, we found that ResNet18 had the highest scores on all the metrics, with the validation accuracy of above 98%, closely accompanied by the ResNet50 that, although it performed well as well, needed more epochs for convergence. The implications of this study for the development of future work in the gender classification technology include the discovery of ethnical, dependable, and effective techniques. Through the consideration of the state of the art and case studies, stakeholders can optimise the efficacy and the accountability of such systems, and thus support societal gains as a result of the improvement in technology.