Currently there is no complete face recognition system that is invariant to all facial expressions.
Although humans find it easy to identify and recognise faces regardless of changes in illumination,
pose and expression, producing a computer system with a similar capability has proved to
be particularly di cult. Three dimensional face models are geometric in nature and therefore
have the advantage of being invariant to head pose and lighting. However they are still susceptible
to facial expressions. This can be seen in the decrease in the recognition results using
principal component analysis when expressions are added to a data set.
In order to achieve expression-invariant face recognition systems, we have employed a tensor
algebra framework to represent 3D face data with facial expressions in a parsimonious
space. Face variation factors are organised in particular subject and facial expression modes.
We manipulate this using single value decomposition on sub-tensors representing one variation
mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained
environments and still preserves the integrity of the 3D data. The results show improved
recognition rates for faces and facial expressions, even recognising high intensity expressions
that are not in the training datasets.
We have determined, experimentally, a set of anatomical landmarks that best describe facial
expression e ectively. We found that the best placement of landmarks to distinguish di erent
facial expressions are in areas around the prominent features, such as the cheeks and eyebrows.
Recognition results using landmark-based face recognition could be improved with better placement.
We looked into the possibility of achieving expression-invariant face recognition by reconstructing
and manipulating realistic facial expressions. We proposed a tensor-based statistical
discriminant analysis method to reconstruct facial expressions and in particular to neutralise
facial expressions. The results of the synthesised facial expressions are visually more realistic
than facial expressions generated using conventional active shape modelling (ASM). We
then used reconstructed neutral faces in the sub-tensor framework for recognition purposes.
The recognition results showed slight improvement. Besides biometric recognition, this novel
tensor-based synthesis approach could be used in computer games and real-time animation
applications