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1. (WO2019045750) MODÈLE DÉTAILLÉ DE FORME D'ŒIL POUR APPLICATIONS BIOMÉTRIQUES ROBUSTES
Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

WHAT IS CLAIMED IS:

1. A wearable display system comprising:

an infrared light source configured to illuminate an eye of a user; an image capture device configured to capture an eye image of the eye;

non-transitory memory configured to store the eye image; and a hardware processor in communication with the non-transitory memory, the hardware processor programmed to:

receive the eye image from the non-transitory memory;

estimate an eye shape from the eye image using cascaded shape regression, the eye shape comprising a pupil shape, an iris shape, or an eyelid shape; and

perform a biometric application based at least in part on the eye shape.

2. The wearable display system of claim 1 , wherein the hardware processor is further programmed to determine an eye feature based at least in part on the eye shape, wherein the eye feature comprises at least one of a glint from the infrared light source, a blood vessel, an iris feature, or a center of the pupil.

3. The wearable display system of claim 1, wherein the biometric application comprises determination of eye gaze.

4. The wearable display system of claim 3, wherein the eye shape comprises the iris shape, and the hardware processor is programmed to search for glints from the infrared light source that are within the iris shape.

5. The wearable display system of claim 1, wherein the eye shape comprises the pupil shape and the eyelid shape, and the hardware processor is programmed to identify a portion of the pupil that is occluded by the eyelid.

6. The wearable display system of claim 5, wherein the hardware processor is programmed to determine a pupillary boundary based on the pupil shape without the portion of the pupil that is occluded by the eyelid.

7. The wearable display system of claim 1, wherein the eye shape comprises the iris shape and the eyelid shape, and the hardware processor is programmed to identify a portion of the ins that is occluded by the eyelid.

8. The wearable display system of claim 7, wherein the hardware processor is programmed to determine a limbic boundary based on the iris shape without the portion of the iris that is occluded by the eyelid.

9. The wearable display system of claim 1, wherein eye shape comprises the eyelid shape, and the biometnc application comprises determination of eye blink.

10. The wearable display system of claim 9, wherein the hardware processor is programmed to reject or assign a lower weight to the eye image if a distance between an upper eyelid and a lower eyelid is less than a threshold.

1 1. The wearable display system of claim 1, wherein the eye shape comprises a boundary to a pupil, an iris, or an eyelid.

12. The wearable display system of claim 1, wherein the biometric application comprises biometric identification or biometric authentication.

13. The wearable display system of any one of claims 1- 12, wherein to estimate the eye shape from the eye image using cascaded shape regression, the hardware processor is programmed to:

iterate a regression function for determining a shape increment over a plurality of stages, the regression function comprising a shape-indexed extraction function.

14. The wearable display system of claim 13, wherein to iterate the regression function, the hardware processor is programmed to evaluate


for a shape increment ASt at stage t of the iteration, where ft is the regression function at stage t , Φί is the shape-indexed extraction function at stage t, / is the eye image, and 5t-- 1 is the eye shape at stage t-1 of the iteration.

15. The wearable display system of claim 13, wherein the shape-indexed extraction function provides a comparison of eye image values between a pair of pixel locations.

16. A method for training an eye shape calculation engine, the method comprising:

under control of a hardware processor:

receiving a set of annotated training eye images, wherein each image in the set is labeled with an eye shape; and

using a machine learning technique applied to the set of annotated training eye images to learn a regression function and a shape-indexed extraction function, where the regression function and the shape-mdexed extraction function learn to recognize the eye shape.

17. The method of claim 16, wherein the eye shape comprises a shape of a pupil, a shape of an iris, or a shape of an eyelid.

18. The method of claim 16 or claim 17, wherein the regression function and the shape-mdexed extraction function are learned to recognize eye shape according to an iteration of

Δ5έ = /t( t(/, 5t_1)),

for a shape increment ASt at stage t of the iteration, where ft is the regression function at stage t , (Pt is the shape-indexed extraction function at stage t, I is an unlabeled eye image, and 5t„i is the eye shape at stage t-l of the iteration.

19. The method of claim 18, wherein the shape-indexed extraction function provides a comparison of eye image values between a pair of pixel locations.

20. The method of claim 19, wherein the comparison comprises a binary or Boolean value.