Multimodal Demographic Prediction: A Transfer Learning Framework with EfficientNet Model

The exploration of facial features has garnered substantial interest, especially with the evolution of deep learning methods. There are various uses for demographic characteristics like age, gender, and ethnicity, yet researchers haven't extensively investigated the prediction of ethnicity within this domain. This research delves into predicting multiple human attributes using the EfficientNet model in deep learning. Along with human race, the model also focuses on predicting age and gender. The well-regarded EfficientNet model, known for its adaptability and high performance, undergoes extensive training using a varied dataset inclusive of diverse ethnicities, age ranges, and gender. Specifically, we employ a variant of the EfficientNet architecture, B0, renowned for its adaptability and high performance. It covers the classification of human races, age estimation, and gender determination, utilizing the robust UTK Face Dataset sourced from Kaggle. Assessment metrics such as accuracy, Mean Absolute Error (MAE) are employed for this evaluation.