Szczegóły publikacji
Opis bibliograficzny
Geometry-aware recognition of mouth articulations for sign language understanding / Nurzhigit ONGALOV, Bogdan KWOLEK // W: Computational Science – ICCS 2026 workshops : 26th International Conference : Hamburg, Germany, June 29–July 1, 2026 : proceedings , Pt. 2 / eds. Maciej Paszynski, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 16787 ). — ISBN: 978-3-032-29908-6; e-ISBN: 978-3-032-29909-3. — S. 519–534. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-06-28
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168607 |
|---|---|
| Data dodania do BaDAP | 2026-07-01 |
| DOI | 10.1007/978-3-032-29909-3_37 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2026 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Recognition of vowel-related mouth articulations in continuous Japanese Sign Language (JSL) signing requires modeling fine-grained geometric structure and temporally coherent lip dynamics. We formulate this task as a structured spatio-temporal graph learning problem over lip landmark trajectories. A geometry-aware multi-branch ST-GCN architecture is proposed, operating on 41 nose-anchored facial landmarks and enriched with linear and angular motion descriptors to capture both spatial configuration and dynamic deformation. Experiments on a publicly available JSL dataset demonstrate that the proposed Lip-STGCN outperforms baseline ST-GCN and tree-based models under a strict cross-subject evaluation protocol, achieving a macro F1-score of approximately 63%. Ablation analysis confirms that jointly modeling structured geometry and motion dynamics is essential for robust vowel articulation recognition in continuous signing.
