Leveraging Computer Vision for Gender, Age, and Ethnicity Prediction Using Deep CNN

With science and technology taking quantum leaps every day, a lot of progress has been made in the field of deep networks and computer vision. Identifying various features from input face images to draw meaningful information and critical insights has garnered much interest. Using these features and images, AI models can provide real-time feedback and alerts for appropriate interventions in health. However, these results lack sufficient accuracy due to the convoluted network architecture and complexity of time regarding the weight suboptimal solution. This chapter aims to create a model that predicts age, gender, and ethnicity using the UTKFace Dataset. Post the data cleaning and label extraction, various neural network architectures were trained, and the performances of these models were evaluated to conduct a comparative analysis. The study demonstrated that ResNet-50 will facilitate the creation of a robust and efficient model for the purpose of gender prediction while EfficientNet B0 could be deployed to enhance the performance of age and ethnicity prediction. Combining such information with AI models can help to develop predictive algorithms that assess an individual’s risk of developing certain diseases or conditions based on the mentioned demographic factors.