Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer Learning

The detection of gender, age, and ethnicity using machine learning techniques holds significant promise for applications in marketing, healthcare, and law enforcement. Previous methods relied on conventional machine learning and manual feature engineering, which often struggled to capture the complex relationships between facial features and these demographic factors. To overcome these limitations, our novel approach leverages the powerful YOLOv5 framework, integrating transfer learning and deep neural networks. This cutting-edge method excels at accurately classifying gender, age, and ethnicity from facial images, thanks to its deep neural network architecture with advanced object detection capabilities. Our work represents a substantial advancement in gender, age, and ethnicity prediction, showcasing the potential of advanced machine learning techniques.