Face Reconstruction from Skull Shapes and Physical Attributes

Reconstructing a person’s face from its skeletal remains is a task that has over many decades fascinated artist and scientist alike. In this paper we treat facial reconstruction as a machine learning problem. We use separate statistical shape models to represent the skull and face morphology. We learn the relationship between the parameters of the models by fitting them to a set of MR images of the head and using ridge regression on the resulting model parameters. Since the facial shape is not uniquely defined by the skull shape, we allow to specify target attributes, such as age or weight. Our experiments show that the reconstruction results are generally close to the original face, and that by specifying the right attributes the perceptual and measured difference between the original and the predicted face is reduced.

• Type: Conference
• Year: 2009
• Where: DAGM
• Authors: Pascal Paysan, Marcel Lüthi, Thomas Albrecht, Anita Lerch, Brian Amberg, Francesco Santini 0002, Thomas Vetter
• Keywords: DAGM 2009, Decades Fascinated Artist, Image Processing, Machine Learning Problem, Separate Statistical Shape