Facial Recognition in Collaborative Learning Videos
Facial Recognition in Collaborative Learning Videos
复制标题
DOI：
10.1007/978-3-030-89131-2_23
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
PhuongThao Tran;M. Pattichis;Sylvia Celedón-Pattichis;Carlos López Leiva
中科院分区：
文献类型：
--
作者：
PhuongThao Tran;M. Pattichis;Sylvia Celedón-Pattichis;Carlos López Leiva
协作学习视频中的人脸识别提出了许多挑战。在协作学习视频中，学生们围坐在一张典型的桌子周围，在录制摄像机的不同位置，来来去去，四处走动，部分或完全被遮挡。此外，视频往往是非常长的，需要开发快速和准确的方法。我们开发了一个动态的系统，识别参与者的协作学习系统。我们解决遮挡和识别失败，通过使用过去的信息，人脸检测的历史。我们解决了需要从不同的姿势和速度的需要，通过采样或K-means clustering计算出的原型脸的集合相关联的每个参与者检测人脸。我们的结果表明，该系统被证明是非常快速和准确的。我们还将我们的系统与使用InsightFace [2]和原始训练视频片段的基线系统进行了比较。与基线系统的70.8%相比，我们实现了86.2%的平均准确度。平均而言，我们的识别率比基线系统快28.1倍。
Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate methods.We develop a dynamic system of recognizing participants in collaborative learning systems. We address occlusion and recognition failures by using past information about the face detection history. We address the need for detecting faces from different poses and the need for speed by associating each participant with a collection of prototype faces computed through sampling or K-means clustering.Our results show that the proposed system is proven to be very fast and accurate. We also compare our system against a baseline system that uses InsightFace [2] and the original training video segments. We achieved an average accuracy of 86.2% compared to 70.8% for the baseline system. On average, our recognition rate was 28.1 times faster than the baseline system.