Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal.
翻译:最近,事件摄像头在几个计算机视觉领域显示了巨大的适用性,特别是涉及高时间分辨率任务。在这项工作中,我们通过提供神经形态事件驱动的面部表情识别数据集NEFER,研究这种数据用于情感识别的用途。 NEFER由成对的RGB和事件视频组成,代表用相应情感标记的人脸,并带有面部包围框和面部标志注释。我们详细介绍了数据采集过程,并提供了RGB和事件数据的基线方法。收集的数据捕捉微小表情,这些表情在RGB数据中很难发现,但在事件域中出现。我们报告事件驱动方法的两倍识别精度,证明了一种神经形态方法用于分析快速且难以检测到的表情及其隐藏的情感的有效性。