Machine Learning, Deep Learning, and Computer Vision for Age and Gender Detection

Predicting the gender and the age of a given individual based on a picture or a video sequence is a computer vision challenge that has received a lot of focus in the latest years from scientists and researchers. Actually, we are increasingly surrounded by data in visual and multimedia form from various sources: social networks, virtual meetings, and chat apps. Gender and age prediction may help in solving various challenges like enhancing surveillance and security networks, improving content recommendation systems, and many convenient projects. However, predicting gender and age from an image is a complex task that requires considerable computing and memory resources. In this context, many researchers are trying to use artificial intelligence and deep learning techniques to uncover more powerful models. The purpose of this document is to develop an effective and optimal model that provides accurate predictions while employing a fast training process and limited available resources. The obtained results show that convolutional neural networks achieve better outcomes than classical machine learning approaches. For gender recognition, the first machine learning model produced an accuracy of 66.32%, and the second one produced an accuracy of 75.46%. For the convolutional neural network model, we obtained an accuracy of 81.72%. For age prediction the first machine learning model produced an error value of 28.27, the second one generated an error value of 28.24 and the convolutional neural network model produced an error value of 20.55.