Unconstrained Face Detection and Open-Set Face Recognition Challenge

Günther, M. et al. (2018) Unconstrained Face Detection and Open-Set Face Recognition Challenge. In: IEEE International Joint Conference on Biometrics (IJCB 2017), Denver, CO, USA, 1-4 Oct 2017, pp. 697-706. ISBN 9781538611241 (doi: 10.1109/BTAS.2017.8272759)

Abstract

Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face recognition have surpassed human capabilities on some datasets, open set identification is much more complex as it needs to reject both unknown identities and false alarms from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false detction rates. However, open set face recognition is currently weak and requires much more attention.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yang, Dr Shufan
• Authors: Günther, M., Hu, P., Herrmann, C., Chan, C. H., Jiang, M., Yang, S., Dhamija, A., Ramanan, D., Beyerer, J., Kittler, J., Al Jazaery, M., Nouyed, M., Guo, G., Stankiewicz, C., and Boult, T. E.
• College/School: College of Science and Engineering > School of Engineering
• ISSN: 2474-9699
• ISBN: 9781538611241
• Published Online: 01 February 2018

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