500,000 images closer to eyelid and pupil segmentation

We propose a fully convolutional neural networkfor pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss forsegmentation and L1 loss for landmark and ellipsis regression. The ap-plication of the proposed network is the offline processing and creationof datasets. Which can be used to train resource-saving and real-timemachine learning algorithms such as random forests. In addition, we willprovide the worlds largest eye images dataset with more than 500,000images.

Human gaze behavior is not the only important aspect abouteye tracking. The eyelids reveal additional important information; suchas fatigue as well as the pupil size holds indications of the workload.The current state-of-the-art datasets focus on challenges in pupil centerdetection, whereas other aspects, such as the lid closure and pupil size,are neglected. Therefore, we propose a fully convolutional neural networkfor pupil and eyelid segmentation as well as eyelid landmark and pupilellipsis regression. The network is jointly trained using the Log loss forsegmentation and L1 loss for landmark and ellipsis regression. The ap-plication of the proposed network is the offline processing and creationof datasets. Which can be used to train resource-saving and real-timemachine learning algorithms such as random forests. In addition, we willprovide the worlds largest eye images dataset with more than 500,000images.

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