Visual ageing of human faces in three dimensions using morphable models and projection to latent structures

We present an approach to synthesising the effects of ageing on human face images using three-dimensional modelling. We extract a set of three dimensional face models from a set of two-dimensional face images by fitting a Morphable Model. We propose a method to age these face models using Partial Least Squares to extract from the data-set those factors most related to ageing. These ageing related factors are used to train an individually weighted linear model. We show that this is an effective means of producing an aged face image and compare this method to two other linear ageing methods for ageing face models. This is demonstrated both quantitatively and with perceptual evaluation using human raters.Postprin

Prototyping and transforming facial textures for perception research

Transforming facial images along perceived dimensions (such as age, gender, race, or health) has application in areas as diverse as psychology, medicine, and forensics. We can use prototype images to define the salient features of a particular face classification (for example, European female adult or East-Asian male child). We then use the differences between two prototypes to define an axis of transformation, such as younger to older. By applying these changes to a given input face, we can change its apparent age, race, or gender. Psychological investigations reveal a limitation with existing methods that's particularly apparent when changing the age of faces. We relate the problem to the loss of facial textures (such as stubble and wrinkles) in the prototypes due to the blending process. We review the existing face prototyping and transformation methods and present a new, wavelet-based method for prototyping and transforming facial textures.Publisher PDFPeer reviewe

A genetic algorithm for face fitting

Accurate estimation of the shape of human faces has many applications from computer-imaging to psychological research. One well known method is to fit a Three Dimensional Morphable Model to a target image. This method is attractive as the faces it constructs are already projected onto an orthogonal basis making further manipulation and analysis easier. So far its use in these fields has been limited the inaccuracy and inconvenience of current face-fitting methods. We present a method based on Genetic Algorithms that avoid the local minima and gradient image errors that current methods suffer from. It has the added advantage of requiring no manual interaction to initialise or guide the fitting process.PostprintPeer reviewe