Eye Contact Detection Algorithms Using Deep Learning and Generative Adversarial Networks
Eye Contact Detection Algorithms Using Deep Learning and Generative Adversarial Networks
复制标题
使用深度学习和生成对抗网络的眼神接触检测算法
DOI：
10.1109/smc.2018.00666
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Nakazawa Atsushi
中科院分区：
文献类型：
--
作者：
Mitsuzumi Yu;Nakazawa Atsushi
目光接触（相互凝视）是人类交流和社会互动的基础;因此，它在心理学，社会科学和医学等许多领域都有研究。我们的小组已经研究了可穿戴的基于视觉的目光接触检测技术，使用第一人称相机的目的是评估在温柔的痴呆症护理凝视技能。在这项工作中，我们从少量的标记图像中寻找基于深度学习的目光接触检测技术。我们实现并测试了两种目光接触检测算法：基于朴素深度学习的算法和基于生成对抗网络（GAN）的半监督学习（SSL）算法。利用哥伦比亚凝视数据集、Facescrub和我们的原始数据集对这些方法进行了学习和验证。结果显示了基于深度学习和基于GAN的方法的有效性和局限性。有趣的是，我们发现眼睛接触检测的准确性相对于相对于相机的面部姿势的双边差异，这预计是由学习数据集引起的。
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.
影响因子：
9
作者：
Zeng, Yi-Ke;Yang, Zu-Li;Cai, Ling
通讯作者：
Cai, Ling