A Comparative Evaluation of Regression Learning Algorithms for Facial Age Estimation

The problem of automatic age estimation from facial images poses a great number of challenges: uncontrollable environment, insufficient and incomplete training data, strong person-specificity, and high within-range variance, among others. These difficulties have made researchers of the field propose complex and strongly hand-crafted descriptors, which make it difficult to replicate and compare the validity of posterior classification and regression schemes. We present a practical evaluation of four machine learning regression techniques from some of the most representative families in age estimation: kernel techniques, ensemble learning, neural networks, and projection algorithms. Additionally, we propose the use of simple HOG descriptors for robust age estimation, which achieve comparable performance to the state-of-the-art, without requiring piecewise facial alignment through tens of landmarks, nor fine-tuned and specific modeling of facial aging, nor additional demographic annotations such as gender or ethnicity. By using HOG descriptors, we discuss the benefits and drawbacks among the four learning algorithms. The accuracy and generalization of each regression technique is evaluated through cross-validation and cross-database validation over two large databases, MORPH and FRGC.