In this paper, we explore bilinear and elastically deformable models for addressing jointly 3D face and facial expression recognition. An elastically deformable model is built first to allow anatomically valid point-to-point correspondence among face surfaces and then, bilinear models are used to decouple the impact of identity and expression on face appearance. This enables the representation of the surface using two independent sets of control coefficients that can be used for joint face and facial expression recognition. The proposed system was tested on the publicly available BU-3DFE face database where an average facial expression recognition rate of 89.5% and a rank-1 face recognition rate of 85% were achieved.

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.

In this paper, we explore bilinear models for jointly addressing 3D face and facial expression recognition. An elastically deformable model algorithm that establishes correspondence among a set of faces is proposed first and then bilinear models that decouple the identity and facial expression factors are constructed. Fitting these models to unknown faces enables us to perform face recognition invariant to facial expressions and facial expression recognition with unknown identity. A quantitative evaluation of the proposed technique is conducted on the publicly available BU-3DFE face database in comparison with our previous work on face recognition and other state-of-the-art algorithms for facial expression recognition. Experimental results demonstrate an overall 90.5% facial expression recognition rate and an 86% rank-1 face recognition rate.

The performance of automatic 3-D face recognition can be significantly improved by coping with the nonrigidity of the facial surface. In this paper, we propose a geodesic polar parameterization of the face surface. With this parameterization, the intrinsic surface attributes do not change under isometric deformations and, therefore, the proposed representation is appropriate for expression-invariant 3-D face recognition. We also consider the special case of an open mouth that violates the isometry assumption and propose a modified geodesic polar parameterization that also leads to invariant representation. Based on this parameterization, 3-D face recognition is reduced to the classification of expression-compensated 2-D images that can be classified with state-of-the-art algorithms. Experimental results verify theoretical assumptions and demonstrate the benefits of the geodesic polar parameterization on 3-D face recognition.