title: Towards Open-World Person Re-Identification by One-Shot Group-Based Verification.
abstract: Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods.
authors.forename: Wei-Shi
authors.initials: WS
authors.lastname: Zheng
authors.forename: Shaogang
authors.initials: S
authors.lastname: Gong
authors.forename: Tao
authors.initials: T
authors.lastname: Xiang
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods* / Q000592:standards*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards*