Calibration-Free Gaze Zone Estimation Using Convolutional Neural Network

Abstract

In this paper we propose a gaze zone estimation method using deep learning. Compared with traditional method, our method does not need the procedure of calibration. In the proposed method, a Kinect is used to capture the video of a computer user, which is pre-processed to suppress illumination variations. After that, haar cascade classifier is adopted to detect the face region and eye region. Then, the eye region is used to estimate the gaze zone on the monitor via a trained CNN (Convolution Neural Network). Experimental results show that the proposed method has a high accuracy, which can be applied in human-computer interaction.