---
title: "Learning Cross-View Binary Identities for Fast Person Re-Identification"
authors:
  - "Feng Zheng"
  - "Ling Shao"
doi: ""
url: "https://ijcai.org/proceedings/2016/342"
pdf: "https://ijcai.org/proceedings/2016/0342.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2399-2405"
track: ""
keywords:
---

# Learning Cross-View Binary Identities for Fast Person Re-Identification

Feng Zheng, Ling Shao

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2399-2405.

## Abstract

In this paper, we propose to learn cross-view binary identities (CBI) for fast person re-identification. To achieve this, two sets of discriminative hash functions for two different views are learned by simultaneously minimising their distance in the Hamming space, and maximising the cross-covariance and margin. Thus, similar binary codes can be found for images of a same person captured at different views by embedding the images into the Hamming space. Therefore, person re-identification can be solved by efficiently computing and ranking the Hamming distances between the images. Extensive experiments are conducted on two public datasets and CBI produces comparable results as state-ofthe- art re-identification approaches but is at least 2200 times faster.