Abstract
We present the first purely event-based method for face detection using the high temporal resolution properties of an event-based camera to detect the presence of a face in a scene using eye blinks. Eye blinks are a unique and stable natural dynamic temporal signature of human faces across population that can be fully captured by event-based sensors. We show that eye blinks have a unique temporal signature over time that can be easily detected by correlating the acquired local activity with a generic temporal model of eye blinks that has been generated from a wide population of users. In a second stage once a face has been located it becomes possible to apply a probabilistic framework to track its spatial location for each incoming event while using eye blinks to correct for drift and tracking errors. Results are shown for several indoor and outdoor experiments. We also release an annotated data set that can be used for future work on the topic.
Highlights
This paper introduces an event-based method to detect and track faces from the output of an event-based camera
The novel algorithm for face detection and tracking we propose in this paper is designed to take advantage of the high temporal resolution data representation provided by event-based cameras
State-of-the-art face detection relies on neural networks that are trained on large databases of face images, to cite the latest from a wide literature, readers should refer to Yang et al (2017), Jiang and Learned-Miller (2017), and Sun et al (2018)
Summary
This paper introduces an event-based method to detect and track faces from the output of an event-based camera. The method is compared to existing image-based face detection techniques (Viola and Jones, 2004; Liu et al, 2016; Jiang and Learned-Miller, 2017; Li and Shi, 2019). It is tested on a range of scenarios to show its robustness in different conditions: indoors and outdoors scenes to test for the change in lighting conditions; a scenario with a face moving close and moving away to test for the change of scale, a setup of varying pose and a scenario where multiple faces are detected and tracked simultaneously.
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