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：近年来，事件相机在多个计算机视觉领域展现出广泛适用性，尤其适用于需要高时间分辨率的任务。本研究通过提出神经形态事件驱动面部表情识别数据集（NEFER），探讨此类数据在情感识别中的应用。NEFER包含成对的RGB与事件视频，视频中标注了对应情感标签、人脸边界框及面部关键点。我们详细阐述了数据采集流程，并提供了RGB与事件数据的基线方法。采集的数据捕捉了微小的面部微表情——这类表情在RGB数据中难以察觉，却在事件域中清晰呈现。实验表明，事件驱动方法的识别准确率提升了一倍，证实了神经形态方法在分析快速、难捕捉的表情及其隐含情感方面的有效性。