Simultaneous Prediction of Nationality and Gender from Facial Images Using Deep Learning

In this study, a large dataset comprising 2000 images extracted from Wikipedia was used to classify individuals' gender and nationality. For this purpose, two popular deep learning models, namely VGG16 and a custom Convolutional Neural Network (CNN), were trained using the SGD optimizer with momentum. The results from evaluating the models on the training and validation data indicate that the VGG16 model significantly outperformed the custom CNN model. VGG16 achieved 98% and 99% accuracy in classifying nationality and gender on the training data, and 90% and 91% accuracy on the validation data, respectively. This study demonstrates that deep learning models, particularly VGG16, have a high potential for performing complex image classification tasks, including gender and nationality recognition.

Simultaneous Prediction of Nationality and Gender from Facial Images Using Deep Learning Keywords:

Gender classification , Nationality prediction , Deep learning , Convolutional Neural Networks (CNN) , VGG16