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Title: Unconstrained Face Detection and Open-Set Face Recognition Challenge
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 identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.
Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Award ID(s):
1650474 1066197
Publication Date:
NSF-PAR ID:
10053704
Journal Name:
International Joint Conference on Biometrics
Page Range or eLocation-ID:
697 to 706
Sponsoring Org:
National Science Foundation
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