Eye labeling tool
Ground truth data is an important prerequisite for the development and evaluation of many algorithms in the area of computer vision, especially when these are based on convolutional neural networks or other machine learning approaches that unfold their power mostly by supervised learning. This learning relies on ground truth data, which is laborious, tedious, and error prone for humans to generate. In this paper, we contribute a labeling tool (EyeLad) specifically designed for remote eye-tracking data to enable researchers to leverage machine learning based approaches in this field, which is of great interest for the automotive, medical, and human-computer interaction applications. The tool is multi platform and supports a variety of state-of-theart detection and tracking algorithms, including eye detection, pupil detection, and eyelid coarse positioning.
The graphical user interface of our labeling tool (E). The red box on the left (A) shows the general adjustment settings and the frame counter with position. The right red box (C) shows the labeled eyes, where the circles are movable by mouse. The green box (B) shows the main window buttons and the blue box (D) the buttons needed for eye feature labeling. The slider above the main window is for normalization and the slider on the right side for zooming in the same.