Canthus-Scaled 468-Landmark FaceMesh Framework for Pupillary Distance Estimation Using Nested AutoML Calibration

Pupillary distance (PD) is an important ocular measurement for optical dispensing and vision-related applications, but standard MediaPipe FaceMesh outputs do not provide true pupil-centre or iris-boundary landmarks when only the 468-landmark representation is available. This study proposes a canthus-scaled 468-landmark framework for estimating PD using facial landmarks and Malay young-adult normative palpebral fissure width. A dataset of 44 subjects was used, where each record contained ground-truth PD and 1,404 coordinate values representing 468 FaceMesh landmarks in three dimensions. Since true pupil centres were unavailable, medial and lateral canthus landmarks were used to construct eye-centre proxies and to compute a subject-specific millimetre scale. A direct canthus-scaled proxy was first evaluated as a deterministic baseline, after which canthus-scaled geometric features were used in a nested AutoML calibration framework. Model development used repeated nested cross-validation, with an outer repeated 5-fold design and an inner 4-fold model-selection loop. The direct proxy achieved a mean absolute error (MAE) of 4.26 mm and showed systematic overestimation. The calibrated nested AutoML model improved performance, achieving a subject-level MAE of 3.510 mm, root mean squared error of 4.22 mm, a bias of −0.08 mm, and 75.0% of predictions within ±5 mm. The calibrated nested AutoML model improved overall error and reduced systematic bias compared with the direct canthus-scaled proxy. However, the Bland–Altman limits of agreement remained wide, indicating that the proposed method should be interpreted as an approximate proxy-based estimation approach rather than a substitute for clinical pupillometer- or ruler-based PD measurement. The framework is most relevant for research settings or datasets where only standard 468-landmark FaceMesh data are available, and iris-refined landmarks are absent.

@article{Tamrin2026,
  title     = {Canthus-Scaled 468-Landmark FaceMesh Framework for Pupillary Distance Estimation Using Nested AutoML Calibration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mohd Izzuddin Mohd Tamrin and Sherzod Turaev and Takumi Sase and Mohd Zulfaezal Che Azemin and Tengku Mohd Tengku Sembok},
  doi       = {10.14569/IJACSA.2026.0170643},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170643}
}