An Expression Deformation Approach to Non-rigid 3D Face Recognition

The accuracy of non-rigid 3D face recognition approaches is highly influenced by their capacity to differentiate between the deformations caused by facial expressions from the distinctive geometric attributes that uniquely characterize a 3D face, interpersonal disparities. We present an automatic 3D face recognition approach which can accurately differentiate between expression deformations and interpersonal disparities and hence recognize faces under any facial expression. The patterns of expression deformations are first learnt from training data in PCA eigenvectors. These patterns are then used to morph out the expression deformations. Similarity measures are extracted by matching the morphed 3D faces. PCA is performed in such a way it models only the facial expressions leaving out the interpersonal disparities. The approach was applied on the FRGC v2.0 dataset and superior recognition performance was achieved. The verification rates at 0.001 FAR were 98.35% and 97.73% for scans under neutral and non-neutral expressions, respectively.