关键词: 考场, 视线估计, 生成对抗网络（GAN）, 注视向量

Abstract:

Monitoring of examinee’s abnormal behavior is easy to make the invigilator to feel visual fatigue. In this paper, model based on line of sight estimation is proposed to automatically detect the abnormal behavior in the examination room. In order to reduce the information loss of line of sight, gaze vector is used to represent the size and direction of line of sight. The model consists of three parts：gaze vector generator, gaze synthesis module and discriminator. Image of examinee’s head is given as input to the generator to generate the gaze vector, and then the head position and gaze position of the examinee are input into the synthesis module to obtain the real gaze vector. The head image and the two vectors obtained above are input into a discriminator, and when the generation adversarial mode is optimal, a generator model that generates real values can be obtained. The experimental results show that the performance of this method is better than the several methods compared in multiple test room environments. Compared with those of Lian et al, the results show that the Area Under Curve（AUC） index is increased by 2.6% while Angular error（Ang） and Euclidean distance（Dist） of the model are efficiently reduced by 20.3% and 8.0% respectively.

Key words: examination room, line of sight estimation, Generative Adversarial Networks（GAN）, gaze vector