Lay All Your Morph on UV: 3D Geometry-Aware Canonical UV Representations for Morphing Attack Detection

Morphing attacks remain a critical threat to face recognition systems, particularly in document-style acquisition settings where such manipulations have the most significant operational impact. Most existing detection methods primarily focus on texture-level or pixel-wise inconsistencies, often neglecting geometric cues that more directly reflect morph-induced deformations. This work investigates the use of UV texture maps derived from 3D facial reconstruction as a canonical, geometry-aware representation for morphing attack detection. We hypothesize that UV parametrization can enhance morph detectability by exposing distortions on the reconstructed facial surface and by amplifying mapping inconsistencies propagated through the reconstruction process, including those indirectly influenced by artifacts outside the facial region. Extensive experiments conducted on document-like datasets and across multiple deep learning architectures show that integrating UV-based representations with original images consistently improves the separability between bona fide and morphed samples. Evaluations under challenging cross-dataset and cross-manipulation conditions further confirm the robustness of the proposed approach, which outperforms models trained solely on raw images and achieves superior performance compared to representative state-of-the-art baselines. Overall, these findings indicate that UV texture maps can act as a geometry-induced canonicalization layer for morphing attack detection, largely independent of the underlying classifier or morphing strategy, and represent a promising direction for enhancing the reliability of MAD systems in document-oriented applications.