MS-EVS: Multispectral event-based vision for deep learning based face detection

Saad Himmi, Vincent Parret, Ajad Chhatkuli, Luc Van Gool

Abstract: Event-based sensing is a relatively new imaging modality that enables low latency, low power, high temporal resolution and high dynamic range acquisition. These properties make it a highly desirable sensor for edge applications and in high dynamic range environments. As of today, most event-based sensors are monochromatic (grayscale), capturing light from a wide spectral range over the visible, in a single channel. In this paper, we introduce multispectral events and study their advantages. In particular, we consider multiple bands in the visible and near-infrared range, and explore their potential compared to monochromatic events and conventional multispectral imaging for the face detection task. We further release the first large scale bimodal face detection datasets, with RGB videos and their simulated color events, N-MobiFace and N-YoutubeFaces, and a smaller dataset with multispectral videos and events, N-SpectralFace. We find that early fusion of multispectral events significantly improves the face detection performance, compared to the early fusion of conventional multi-spectral images. This result shows that multispectral events carry relatively more useful information about the scene than conventional multispectral images do, with respect to their grayscale equivalent. To the best of our knowledge, our proposed method is the first exploratory research on multispectral events, specifically including near infrared data.