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This paper presents a unique approach for the dichotomy between useful andadverse variations of key-point descriptors, namely the identity and theexpression variations in the descriptor (feature) space. The descriptorsvariations are learned from training examples. Based on the labels of thetraining data, the equivalence relations among the descriptors are established.Both types of descriptor variations are represented by a graph embedded in thedescriptor manifold. The invariant recognition is then conducted as a graphsearch problem. A heuristic graph search algorithm suitable for the recognitionunder 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 recognitionperformance, under expression variations in particular, by considerablemargins.

Learning Descriptors Invariance Through Equivalence Relations Within Manifold: A New Approach to Expression Invariant 3D Face Recognition