Geometrically constrained, unsupervised training of convolutional autoencoders for extraction of eye landmarks
US12093835B2 · kind B2 · utility
Inventors
Key dates
| Filing date | Oct 23, 2022 |
| Grant date | Sep 17, 2024 |
| Priority date | — |
| Expiry date | Oct 23, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20132
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Unsupervised, deep learning of eye-landmarks in a user-specific eyes' image data by capturing an unlabeled image comprising an eye region of a user, using an initial geometrically regularized loss function, training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of the user to recover a plurality of user-specific eye landmarks, training a convolutional neural network for autoencoded landmarks-based recovery from the unlabeled image, and where the initial geometrically regularized loss function is represented by the formula LAE=λreconLrecon+λconcLconc+λsepLsep+λeqvLeqv where LAE is total AutoEncoder Loss, λreconLrecon is λ-weighted reconstruction loss, λconcLconce is λ-weighted concentration loss, λsepLsep is λ-weighted separation loss, and λeqvLeqv is λ-weighted equivalence loss.
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