Extending Face Identification to Open-Set Face Recognition

Face identification plays an important role in biometrics and surveillance. However, before applying face id1entification methods in real scenarios, we have to determine whether the subject in a test sample is known (enrolled in the face gallery). In this work, we focus on approaches to determine whether a given face sample belongs to a subject enrolled in the face gallery. We show how the approaches can be combined with face identification methods so they can perform open-set face recognition. Among the five approaches described in this work, four are based on responses from the face identification, and one is based on comparisons between known samples and samples from an independent background set. The approaches differ on features explored in the data, scalability and accuracy. We evaluate the proposed approaches in two standard and challenging datasets for face recognition (FRGC and PubFig83). Results considering different number of enrolled subjects show which approach can be considered in scenarios where, for instance, one is interested in recognizing few wanted subjects.