A Method of Disentanglement of Latent Factor using Geometric Feature for Gaze Estimation Network Training

Abstract

Since each human eye has different anatomical features, gaze estimation is a very challenging task. Although numerous studies regarding gaze estimation were proposed, there is a need for improving the preciseness in order to facilitate the application of the method to real-world scenarios. To accomplish this goal, I propose a novel training strategy for gaze representation learning. The proposed training method includes two training phases: the autoencoder-based representation learning phase and the gaze estimation network training phase. The proposed training strategy enforces the trained model to disentangle the gaze-related latent code and produce a more accurate gaze estimation. In addition, I also propose and showcase a real-world application that exploits the proposed method in order to prove the practicality of the proposed method. Through the experiment, it is proven that the proposed method shows an outstanding performance compared to other methods on the Gaze360 dataset.