Real-Time Eye-Gaze Based Interaction for Human Intention Prediction and Emotion Analysis
Hao He, Yingying She, Jianbing Xiahou, Junfeng Yao, Jun Li, Qingqi Hong, Yingxuan Ji · 2018
The human eye's state of motion and content of interest can express people's cognitive status and emotional status based on their situation. When observing the surrounding things, the human eyes make different eye movements according to the observed objects which reflects human's attention and interest. In this paper, we capture and analyze patterns of human eye-gaze behavior and head motion and classify them into different categories. Besides, we compute and train the eye-object movement attention model and eye-object feature preference model based on different peoples' eye-gaze behaviors by using machine learning algorithms. These models are used to predict humans' object of interest and the interaction intention according to people's real-time situation. Furthermore, the eye-gaze behavior and head motion patterns can be used as a modality of non-verbal information in the computing of human emotional states based on the PAD affective computing model. Our methodology analyzes human emotion and cognition status from the aspect of eye-gaze behavior and head motion, understands the cognitive information that human eyes can express, and effectively improves the efficiency of human-computer interaction in different circumstances.