Gender and Age Estimation from Synthetic Face Images

Our ability to recognize the gender and estimate the age of people around us is crucial for our social development and interactions. In this paper, we investigate how to use Slow Feature Analysis (SFA) to estimate gender and age from synthetic face images. SFA is a versatile unsupervised learning algorithm that extracts slowly varying features from a multidimensional signal. To process very high-dimensional data, such as images, SFA can be applied hierarchically. The key idea here is to construct the training signal such that the parameters of interest, namely gender and age, vary slowly. This makes the labelling of the data implicit in the training signal and permits the use of the unsupervised algorithm in a hierarchical fashion. A simple supervised step at the very end is then sufficient to extract gender and age with high reliability. Gender was estimated with a very high accuracy, and age had an RMSE of 3.8 years for test images.