BWCFace: Open-set Face Recognition using Body-worn Camera
Ali Almadan, Anoop Krishnan, Ajita Rattani

TL;DR
This paper introduces BWCFace, a large dataset for body-worn camera face recognition and evaluates deep learning models for open-set identification, highlighting the importance of fine-tuning for improved accuracy.
Contribution
The work provides a new dataset of 178K facial images from body-worn cameras and assesses CNN architectures with various loss functions for face recognition in this context.
Findings
Maximum 33.89% Rank-1 accuracy with off-the-shelf SENet-50.
Accuracy improves to 99.00% after fine-tuning on BWCFace.
Performance consistent across different body-worn camera sensors.
Abstract
With computer vision reaching an inflection point in the past decade, face recognition technology has become pervasive in policing, intelligence gathering, and consumer applications. Recently, face recognition technology has been deployed on bodyworn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic using traditional techniques on datasets with small sample size. This paper aims to bridge the gap in the state-of-the-art face recognition using bodyworn cameras (BWC). To this aim, the contribution of this work is two-fold: (1) collection of a dataset called BWCFace consisting of a total of 178K facial images of 132 subjects captured using the body-worn camera in in-door and daylight conditions, and (2) open-set evaluation of the latest deep-learning-based Convolutional Neural…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
