Eye Contact Detection Algorithms Using Deep Learning and Generative Adversarial Networks

Eye contact (mutual gaze) is a foundation of human communication and social interactions; therefore, it is studied in many fields such as psychology, social science, and medicine. Our group have been studied wearable vision-based eye contact detection techniques using a first person camera for the purpose of evaluating the gaze skills in the tender dementia care. In this work, we search for deep learning-based eye contact detection techniques from small number of labeled images. We implemented and tested two eye contact detection algorithms: naïve deep-learning-based algorithm and generative adversarial networks (GAN)-based semi supervised learning (SSL) algorithm. These methods are learned and verified by using Columbia Gaze Dataset, Facescrub and our original datasets. The results show the effectiveness and limitations of the deep-learning-based and GAN-based approaches. Interestingly, we found the bilateral difference of the accuracy of eye contact detection with respect to the facial pose with respect to the camera, which is expected to be caused by the learning datasets.