Direct face detection and video reconstruction from event cameras
Abstract
Event cameras are emerging as a new class of cameras, to potentially rival conventional CMOS cameras, because of their high speed operation and low power consumption. Pixels in an event camera operate in parallel and fire asynchronous spikes when individual pixels encounter a change in intensity that is greater than a pre-determined threshold. Such event-based cameras have an immense potential in battery-operated or always-on application scenarios, owing to their low power consumption. These event-based cameras can be used for direct detection from event streams, and we demonstrate this potential using face detection as an example application. We first propose and develop a patch-based model for the event streams acquired from such cameras. We demonstrate the utility and robustness of the patch-based model for event-based video reconstruction and event-based direct face detection. We are able to reconstruct images and videos at over 2,000 fps from the acquired event streams. In addition, we demonstrate the first direct face detection from event streams, highlighting the potential of these event-based cameras for power-efficient vision applications.
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Event data can be decompressed to retrieve a standard gray-scale visual representation, including via deep learning-based event-to-image methods.
“Recently, deep neural network-based event-to-image conversion models [8]-[11] have demonstrated impressive abilities in recovering gray-scale images from event streams, posing a potential risk to the privacy aspect of event-based vision applications.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [36]
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Deep Polarization Reconstruction with PDAVIS Events
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Detection of Binary Square Fiducial Markers Using an Event Camera
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NeuroDFD: Towards Efficient Driver Face Detection with Neuromorphic Vision Sensor
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Events-To-Video: Bringing Modern Computer Vision to Event Cameras
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Unsupervised Event-Based Video Reconstruction
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Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging
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- Authors
- Miyatani, Y., Barua, S., & Veeraraghavan, A.
- Affiliations
- Rice University (US)
- Venue
- 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2016-03
- Volume
- 2016
- Pages
- 1-9
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2403865344
- Cited by
- 133
- References
- 37
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 19 · 2023: 18 · 2022: 8 · 2021: 20 · 2020: 21 · 2019: 18 · 2018: 3 · 2017: 2 · 2016: 2
- FWCI
- 2.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- IEEE Conference Proceedings
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- event cameras, event-based vision, face detection, video reconstruction, high-speed imaging, low-power vision, event stream processing
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