3D age progression prediction in children's faces with a small exemplar-image set

This work aims to develop a system for predicting age progression in children's faces from a small exemplar-image set, which is a critical task to assist in the search for missing children. The proposed method consists of a facial component extraction module, a facial component distance measurement module, and a face synthesis module. It is developed based on the assumption that two similar facial components of two children will retain similar when they grow up. Two different distance measures, namely the learning-based Mahalanobis distance and the curvature-weighted plus bending-energy distance, are employed to select similar facial components from an aging database. The growth curve of each facial component is used to predict the shape, size, and location of each component at a different age. The thin plate spline method is applied to synthesize a 3D face model from the predicted components by minimizing the bending energy. Experiments are conducted to test the proposed method with various subjects and the results show that the proposed method yields promising results.