Space-Time Triplet Loss Network for Dynamic 3D Face Verification

In this paper, we propose a new approach for 3D dynamic face verification exploiting 3D facial deformations. First, 3D faces are encoded into low-dimensional representations describing the local deformations of the faces with respect to a mean face. Second, the encoded versions of the 3D faces along a sequence are stacked into 2D arrays for temporal modeling. The resulting 2D arrays are then fed to a triplet loss network for dynamic sequence embedding. Finally, the outputs of the triplet loss network are compared using cosine similarity measure for face verification. By projecting the feature maps of the triplet loss network into attention maps on the 3D face sequences, we are able to detect the space-time patterns that contribute most to the pairwise similarity between differ-ent 3D facial expressions of the same person. The evaluation is conducted on the publicly available BU4D dataset which contains dynamic 3D face sequences. Obtained results are promising with respect to baseline methods.