Unconstrained Face Detection and Open-Set Face Recognition Challenge
This addresses the challenge of identifying people from surveillance cameras with diverse conditions, but it is incremental as it builds on existing benchmarks.
The paper tackled the problem of unconstrained face detection and open-set face recognition in outdoor surveillance, showing that detection can achieve high rates with moderate false accepts, while recognition remains weak and needs more focus.
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.