Extracting biometric information from an eye generally includes a procedure for the segmentation of the iris within an eye image. In addition to this segmentation of the iris, the portion of the iris that is occluded by the eyelids (upper or lower) can be estimated. This estimation is performed because, during normal human activity, the entire iris of a person is rarely visible. Estimating the portion of the iris occluded by the eyelids has presented challenges. Embodiments of eyelid shape estimation described herein advantageously can be used for estimating the portion of the iris occluded by eyelids. Systems and methods for eyelid shape estimation are disclosed. In one aspect, after receiving an eye image of an eye (e.g., from an image capture device), an eye pose of the eye in the eye image is determined. From the eye pose, an eyelid shape (of an upper eyelid or a lower eyelid) can be estimated using an eyelid shape mapping model. The eyelid shape mapping model relates the eye pose and the eyelid shape. In another aspect, the eyelid shape mapping model is learned (e.g., using a neural network).
