From apparent to real age: Gender, age, ethnic, makeup, and expression bias analysis in real age estimation

Real age estimation in still images of faces is an active area of research in the computer vision community. However, very few works attempted to analyse the apparent age as perceived by observers. Apparent age estimation is a subjective task, which is affected by many factors present in the image as well as by observer's characteristics. In this work, we enhance the APPA-REAL dataset, containing around 8K images with real and apparent ages, with new annotated attributes, namely gender, ethnic, makeup, and expression. Age and gender from a subset of guessers is also provided. We show there exists some consistent bias for a subset of these attributes when relating apparent to real age. In addition we run simple experiments with a basic Convolutional Neural Network (CNN) showing that considering apparent labels for training improves real age estimation rather than training with real ages. We also perform bias correction on CNN predictions, showing that it further enhance final age recognition performance.