3D SPARSE DEFORMATION SIGNATURE FOR DYNAMIC FACE RECOGNITION

This paper proposes a novel compact and memory efficient Sparse 3D Deformation Signature (S3DS) to represent a sparse 3D deformation signal for 3D Dynamic Face Recognition. S3DS is based on a non-linear 6D-space representation that secures physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh, thanks to a recent 3D Deformation Signature (3DS) that is based on Lie Bodies. The proposed S3DS sparsely concatenates unique triangular indicators to construct the facial signature for each temporal instance. The novel descriptor shall benefit domains like surveillance and security in providing non-intrusive bio-metric measurements. By construction, S3DS is resistant to common security attacks like presentation, template and adversarial attacks. Two dynamic datasets (BU4DFE and COMA) were examined in various sparse concatenation settings. Using high reduction rates of≈500, a first rank recognition accuracy similar to the state of the art was achieved. At low reduction rates of ≈40, S3DS outperformed most existing literature on BU4DFE achieving 99.92%. On COMA, it achieved 99.93% which outperforms existing literature. In an open-world experimental setup, using thousands of distractors, the accuracy reached up to 100% in detecting unseen distractors with high reduction rates in the 3D facial descriptor size.