  This paper presents a unique approach for the dichotomy between useful and
adverse variations of key-point descriptors, namely the identity and the
expression variations in the descriptor (feature) space. The descriptors
variations are learned from training examples. Based on the labels of the
training data, the equivalence relations among the descriptors are established.
Both types of descriptor variations are represented by a graph embedded in the
descriptor manifold. The invariant recognition is then conducted as a graph
search problem. A heuristic graph search algorithm suitable for the recognition
under this setup was devised. The proposed approach was tests on the FRGC v2.0,
the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition
performance, under expression variations in particular, by considerable
margins.

Computer Vision and Pattern Recognition