biometric security teams

Abstract: Aligning a partial, real-world 3D face scan to a canonical representation requires solving two coupled problems at once: estimating the rigid pose that brings the scan into a common frame, and completing the facial shape in regions the scan never observed. Existing pipelines typically decouple these steps, running a generic point-cloud registration front end followed by a separate non-rigid fitting or deformation stage. Building on the GeoTransformer superpoint-matching architecture, we propose a single network that predicts both a rigid pose and a shape-complete point cloud in one forward pass: a cross-attention regressor, conditioned on transformer point features and predicted coarse correspondences, maps a partial scan to per-patch coefficients of a 3D morphable face model learned offline from a corpus of 8,000 identities. The resulting morphed template is then used, in the same forward pass, as the target for fine-grained Sinkhorn matching and rigid pose estimation, supervised purely by geometric alignment to ground-truth morphed geometry, so no hand-labeled coefficients are required. Against six classical and learned non-rigid registration baselines on held-out identities, our method leads the strongest ones by 3-4 times on per-vertex error and baselines that collapse the head onto the visible region by more than an order of magnitude, and is simultaneously the most accurate and fastest method on ground-truth-free scans from two independent real-scan datasets, NoW and NPHM. Code will be made publicly available.

Abstract: Eye-movement authentication in virtual reality (VR) needs longitudinal training data, yet most such datasets come from lab trackers. We convert lab recordings to a VR headset's signal quality while keeping their eye movement, using only unpaired VR recordings of other people. Measurements show that the headset's added noise dominates above 7.8 Hz and that its quality is consistent within a person. A parametric device model re-creates the headset's sampling, a person-consistent calibration error and spectrally shaped noise, and a frequency-locked diffusion renderer re-textures that high-frequency noise. Converted GazeBase recordings match GazeBaseVR in noise level and accuracy, keep their source's saccade dynamics, and are harder to tell from real VR data than two prior conversions. An authenticator trained only on converted data verifies unseen VR users at a 5.9% equal error rate with 45 s of gaze, close to real VR training (5.8%); with 5 s, a gap remains.