The problem of open-set face recognition determines whether the subject’s picture presented to the recognition system belongs to a known individual (person in the gallery). This problem is of great importance to find suspects in public environment. For instance, an airport or a train station have a list of wanted subjects (watch-list) that should be recognized when they walk in the monitored space. However, at the same time the face recognition system cannot mistakenly recognize an unwanted person as a wanted one (false alarms). This is the focus of our paper entitled Towards Open-Set Face Recognition using Hashing Functions that was presented in The International Joint Conference on Biometrics (IJCB 2017) and received the Best Paper Runner-up Award.
Open-set face recognition has a large room for improvement since only few researchers have focused on that subject. In fact, a real-world recognition system has to cope with several unseen individuals at real time and determine whether a given face image is associated with a subject registered in a gallery of known individuals. In this work, we combine hashing functions and classification methods to find out when probe samples are known.
The next figure illustrates two different queries: one corresponds to querying an enrolled individual and the other searches for an unknown subject. Notice that there is a highlighted bin when the query image has a matching identity. It probably represents the probe sample’s identity. On the contrary, several bins are incremented when no subject from the gallery correlates to the probe sample.