Pupil localization is one of the most critical and essential requirements for eye gaze estimation and eye movement tracking. Because pupil images contain monotonous and uncomplicated information, the dataset uses a single class of labels to describe the image content, and using convolutional neural networks can quickly and accurately identify the pupil position on the input image. On low-resolution images, traditional methods encounter issues of low accuracy and cumbersome design steps. A lightweight pupil localization algorithm is proposed in this paper, utilizing a convolutional neural network (CNN) with additional training samples. The experimental results demonstrate the algorithm’s significant effectiveness in identifying the pupil position within the training set, with the accuracy of pupil position in the test set reaching 97.78%. This provides an evidence of the algorithm’s feasibility for accurately localizing pupils in low-resolution images.
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