Gaze Estimation by Attention Using a Two-Stream Regression Network

Ahmet Karazor, Alperen Enes Bayar, Cihan Topal, Hakan Çevikalp

Abstract: Determining the point of view of people is an important human-computer interaction problem that has been studied for a long time. This subject, which has many applications, is used in different fields such as marketing, automotive, medical, games and entertainment. In this study, we propose a remote eye tracking method that makes gaze estimation using convolutional neural network based on regression. The proposed method uses a two-stream deep learning architecture that utilizes eye images and iris segmentation masks obtained through segmentation neural network. The architecture employed selective attention-based mechanisms to enhance its performance. Experimental results demonstrate that the attention-based two-stream architecture outperforms both single-stream deep learning architectures and architectures without attention mechanisms.

External IDs:dblp:conf/siu/KarazorBTC23