Estimating people's age using convolutional neural networks

Over the past few years, researchers have created a growing number of machine learning (ML)-based face recognition techniques, which could have numerous interesting applications, for instance, enhancing surveillance monitoring, security control, and potentially even forensic art. In addition to face recognition, advancements in ML have also enabled the development of tools to predict or estimate specific qualities (e.g., gender or age) of a person by analyzing images of their faces. In a recent study, researchers at the University of Kwazulu-Natal, in South Africa, developed a machine learning-based model to estimate people's age by analyzing images of their faces taken in random real-life environments. This new architecture was introduced in a paper published by Spinger and presented a few days ago at the International Conference on Computational Collective Intelligence (ICCCI) 2019. Most traditional approaches for age classification only perform well when analyzing face images taken in controlled environments, for instance, in the lab or in photography studios.