CNN-Based Pupil Center Detection for Wearable GazeEstimation System

This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user’s eye can be used in various applications, suchas human-computer interaction, medical diagnosis, and psychological studies. However, users tend to blink frequently; thus,estimating gaze direction is difficult. The proposed method uses two CNN models. The first CNN model is used to classify theeye state and the second is used to estimate the pupil center position. The classification model filters images with closed eyes and terminates the gaze estimation process when the input image shows aclosed eye. In addition, this paper presents a process to createan eye image dataset using a wearable camera. This data set, which was used to evaluate the proposed method, has approximately20,000 images and a wide variation of eye states. We evaluated the proposed method from various perspectives. The result shows that the proposed method obtained good accuracy and has the potential for application in wearable device-based gaze estimation.