Pose-invariant face recognition by matching on multi-resolution MRFs linked by supercoupling transform

Recognition of faces in arbitrary pose is addressed in this paper. For this task, an MRF-based classification approach is proposed which employs the energy of the established match between a pair of images as a criterion of goodness-of-match. By incorporating an image matching method as part of the recognition process, the system is made robust to moderate global spatial transformations. The approach draws on a method [1] which has the potential to cope with pose changes but a direct application of which suffers from several shortcomings. In order to overcome these problems, a number of enhancements are proposed. First, by adopting a multi-scale relaxation scheme based on super coupling transform, the inference using sequential tree re-weighted message passing approach [2] is accelerated. Next, by taking advantage of a statistical shape prior for the matching, the results are regularized and constrained, making the system robust to spurious structures and outliers. For classification, both textural and structural similarities of the facial images are taken into account. The method is evaluated on two databases and promising results are obtained.

► The work presents a 2D pose-invariant face recognition system using MRFs.
► The proposed graph-based method obviates the need for non-frontal images for training.
► The MRFs are processed in a multi-resolution fashion using super-coupling transform.
► By employing a statistical shape prior, the matching errors are minimized.
► The distance metric between faces is defined as a normalized energy of a match.