Adversarial binary coding for efficient person re-identification

Person re-identification (ReID) aims at associating persons with the same identity across different views/scenes. Most existing methods improve matching accuracy by proposing high-dimensional real-valued features to represent person images comprehensively. However, considering the increasing data scale in real-world applications, the storage and matching efficiencies should be paid attention to as well. In this paper, we propose a binary coding approach for efficient ReID, inspired by the recent advances in adversarial learning. Specifically, the proposed Adversarial Binary Coding (ABC) implicitly fits the feature distribution to the expected binary one by optimizing the Wasserstein distance. To further enhance the semantic discriminability of binary codes, we seamlessly embed the ABC into a similarity measuring deep neural network. By end-to-end learning the framework, compact and discriminative binary features are generated for efficient and accurate ReID. Extensive experiments on large-scale benchmarks demonstrate the superiority of our approach over the state-of-the-art methods in both efficiency and accuracy.