Gaze estimation using residual neural network

Eye gaze tracking has become an prominent research topic in human-computer interaction and computer vision. It is due to its application in numerous fields, such as the market research, medical, neuroscience and psychology. Eye gaze tracking is implemented by estimating gaze (gaze estimation) for each individual frame in offline or real-time video captured. Therefore, in order to produce the secure the accurate tracking, especially in the emerging use in medical and community, innovation on the gaze estimation posts a challenge in research field. In this paper, we explored the use of the deep learning model, Residual Neural Network (ResNet-18), to predict the eye gaze on mobile device. The model is trained using the large-scale eye tracking public dataset called GazeCapture. We aim to innovate by incorporating methods/techniques of removing the blinking data, applying image histogram normalisation, head pose, and face grid features. As a result, we achieved 3.05cm average error, which is better performance than iTracker (4.11cm average error), the recent gaze tracking deep-learning model using AlexNet architecture. Upon observation, adaptive normalisation of the images was found to produce better results compared to histogram normalisation. Additionally, we found that head pose information was useful contribution to the proposed deep-learning network, while face grid information does not help to reduce test error.