Correcting pupil center shift to compute gaze

A system comprising:

• at least one storage device;
• and
• at least one processor coupled to the at least one storage device and configured to perform operations comprising:
• accessing an uncorrected gaze vector computed based on a center point of a pupil of a user;
• processing an image of the pupil by a machine learning model to predict an estimated error in a gaze vector, the machine learning model trained to establish a relationship between a plurality of ground truth gaze vectors and uncorrected gaze vectors for a plurality of pupil parameters, the pupil parameters including diameters, gaze angles, or gaze eccentricities;
• and
• generating a corrected gaze vector by applying the estimated error in the gaze vector predicted by the machine learning model to the uncorrected gaze vector that has been computed based on the center point of the pupil of the user.

A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

• accessing an uncorrected gaze vector computed based on a center point of a pupil of a user;
• processing an image of the pupil by a machine learning model to predict an estimated error in a gaze vector, the machine learning model trained to establish a relationship between a plurality of ground truth gaze vectors and uncorrected gaze vectors for a plurality of pupil parameters, the pupil parameters including diameters, gaze angles, or gaze eccentricities;
• and
• generating a corrected gaze vector by applying the estimated error in the gaze vector predicted by the machine learning model to the uncorrected gaze vector that has been computed based on the center point of the pupil of the user.