CSA-CNN: A Contrastive Self-Attention Neural Network for Pupil Segmentation in Eye Gaze Tracking
Soumil Chugh, Juntao Ye, Yuqi Fu, Moshe Eizenman · 2024
This paper presents a novel Contrastive Self-Attention Convolutional Neural Network (CSA-CNN) model with enhanced Difficulty Aware (DA) loss function to improve the segmentation of pupils in eye images. The incorporation of transformer-style self-attention and Difficulty-Aware loss in a UNET-style architecture allows for robust feature representation and promotes shape alignment. The novel model was trained on two public databases (LPW and RIT-Eyes) and evaluated on two other public datasets (ExCuSe and ElSe). When compared with seven state-of-the-art pupil center detection methods, the CSA-CNN showed improvement of over 6% in pupil center detection accuracy (detection within 5 pixels of the labeled center) and more than 9% in Intersection Over Union (IOU) accuracy, compared to the best performer among the other seven methods. Furthermore, when the CSA-CNN model was integrated into a glint-based eye tracking system that uses learning based methods to detect pupil-center, we saw a 25% improvement in gaze accuracy.