Structure-Aware Slow Feature Analysis for Age Estimation

As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples. However, most existing efforts to age estimation usually cope with the face samples individually, which do not take full advantage of the temporal structure and contextual structure of the face samples. In this letter, we propose an age estimation approach named structure-aware slow feature analysis, which is capable of effectively capturing the structure of human faces in the aspects of time-related smoothness for progressive age variation as well as face-related attribute constraints for face age consistency. As a result, we present an iterative optimization scheme to effectively learn the slowly varying feature transformation. Experimental results demonstrate the effectiveness of our approach on the Morph dataset.