3D facial expression recognition using swarm intelligence

In this paper, we present a novel approach for 3D facial expression recognition which is inspired by the advances of ant colony and particle swarm optimization (ACO and PSO respectively) in the field of data mining. Anatomical correspondence between faces is first established using a generic 3D face model which is deformed elastically to match the facial surfaces. Surface points are then used as a basis for classification according to a set of classification rules, which are discovered by an ACO/PSO-based rule discovery algorithm. The performance of the proposed algorithm has been evaluated on the BU-3DFEDB facial expression database where a total recognition rate of 92.3% was achieved.