Person Re-Identification Using Multiple Experts with Random Subspaces

This paper presents a simple and effective multi-expert approach based on random subspaces for person re-identification across non-overlapping camera views. This approach applies to supervised learning methods that learn a continuous decision function. Our proposed method trains a group of expert functions, each of which is only exposed to a random subset of the input features. Each expert function produces an opinion according to the partial features it has. We also introduce weighted fusion schemes to effectively combine the opinions of multiple expert functions together to form a global view. Thus our method overall still makes use of all features without losing much information they carry. Yet each individual expert function can be trained efficiently without overfitting. We have tested our method on the VIPeR, ETHZ, and CAVIAR4REID datasets, and the results demonstrate that our method is able to significantly improve the performance of existing state-of-the-art techniques.