EG-SIF: Improving Appearance Based Gaze Estimation using Self Improving Features

Gaze estimation is vital in various applications, but factors like poor lighting and lowresolution images challenge the performance of estimation model. We introduce, for thefirst time a Eye Gaze Estimation with Self-Improving Features (EG-SIF) method. EG-SIFsegregates images based on their quality, generates a pair of good and adverse images, andapplies multitask training with image enhancement using the generated pairs, where thetask is to reconstruct given a poor image. This innovative approach outperforms existingmethods, significantly improving gaze estimation angular error on challenging datasets likeMPIIGaze from 4.64 to 4.53 and in RTGene from 7.44 to 7.41.