Synthesis of facial ageing transforms using three-dimensional morphable models

Abstract

The ability to synthesise the effects of ageing in human faces has numerous uses from aiding the search for missing people to improving recognition algorithms and aiding surgical planning. The principal contribution of this thesis is a novel method for synthesising the visual effects of facial ageing using a training set of three-dimensional scans to train a statistical ageing model. This data-base is constructed by fitting a statistical Face Model known as a Morphable Model to a set of two dimensional photographs of a set of subjects at different age points in their lives. We verify the effectiveness of this algorithm with both quantitative and psychological evaluation. Most ageing research has concentrated on building models using two-dimensional images. This has two major shortcomings, firstly some of the information related to shape change may be lost by the projection to two-dimensions; secondly the algorithms are very sensitive to even slight variations in pose and lighting. By using standard face-fitting methods to fit a statistical face model to the image we overcome these problems by reconstructing the lost shape information, and can use a model of physical rotations and light transfer to overcome the issues of pose and rotation. We show that the three-dimensional models captured by face-fitting offer an effective method of synthesising facial ageing. The second contribution is a new algorithm for ageing a face model based on Projection to Latent Structures also known as Partial Least Squares. This method attempts to separate the training set into a set of basis vectors that best explains the shape and colour changes related to ageing from those factors within the training set that are unrelated to ageing. We show that this method is more accurate than other linear techniques at producing a face model that resembles the individual at the target age and of producing a face image of the correct perceived age. The third contribution is a careful evaluation of three well known ageing methods. We use both quantitative evaluation to determine the accuracy of the ageing method, and perceptual evaluation to determine how well the model performs in terms of perceived age increase and also identity retention. We show that linear methods more accurately capture ageing and identity information if they are trained using an individualised model, and that ageing is more accurately captured if PLS is used to train the model.