Dense and Sparse 3D Deformation Signatures for 3D Dynamic Face Recognition

Abstract

This work analyses dense and sparse 3D Deformation Signatures to represent 3D temporal deformation instances. The signatures are employed in dynamic 3D face recognition, however, they are applicable in other domains. This is demonstrated for dynamic expression recognition. The pushing need for non-intrusive bio-metric measurements made face and its expressions recognition dominant players in domains like entertainment, surveillance and security. The proposed signature can be computed from 2D, 3D or hybrid input by means of robust 3D fitting. It is computed given a non-linear 6D space representation which guarantees by construction physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh as a ratio derived from scale and in-plane deformation in the canonical space. These indicators are concatenated densely or sparsely to form the signature. It is then used to learn the 3D deformation space from the temporal facial signals. Two dynamic datasets were examined for evaluation. The reported 1-Rank recognition accuracy outperforms the existing literature. Democratising the recognition step results in 100% accuracy as demonstrated by the reported confusion matrices. In an open-world setting in the face recognition context, an accuracy of 100% was achieved in detecting intruders. The signature robustness has been further validated in face expressions recognition from a very challenging highly 3D dynamic dataset.