Work of Lanitis et al. used the Active Appearance Model (AAM), a statistical face model, to study age estimation problems. In their approach, after AAM parameters were extracted from face images landmarked with 68 points, an “aging function” was built using Genetic Algorithms to optimize the aging function. Meanwhile, Geng et al. introduced an AGing pattern Subspace (AGES) to estimate the ages of individuals. In order to handle incomplete data such as missing ages in the training sequence, the AGES method models a sequence of individual aging face images by learning a subspace representation. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with a minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age.