4D Analysis of Facial Ageing Using Dynamic Features

Facial ageing analysis based on 4D data (3D plus time) is much more robust to pose changes and illumination variations than using 2D image and video. The purpose of this investigation was to measure the effects of age and gender related facial changes using dynamic 3D facial scans. Experiments were carried out on the subjects, who were divided into two groups by age (15-30 years and 31-60 years). Each group was further subdivided by gender. 3D scans of the subjects were processed to extract facial features which were tracked through the duration of the data capture. Subsequently, a set of dynamic features were computed from these facial features, as well as static features for comparison. Two-way multivariate analysis of variance (MANOVA) of these features demonstrated that statistically significant age and gender related differences could be detected. We show that 3D facial dynamics provide more useful information than static features for the characterisation of smiles.