Two-way constraint network for RGB-Infrared person re-identification

作     者：Zeng, Haitang Hu, Weipeng Chen, Dihu Hu, Haifeng 

作者机构：Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China 

出 版 物：《ELECTRONICS LETTERS》 (电子学快报)

年 卷 期：2021年第57卷第17期

页      面：653-655页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Science and Technology Program of Guangdong Province [2019B010140002] 

主　　题：Optical, image and video signal processing Image sensors Computer vision and image processing techniques Data handling techniques Neural nets 

摘      要：RGB-Infrared person re-identification (RGB-IR Re-ID) is a task aiming to retrieve and match person images between RGB images and IR images. Since most surveillance cameras capture RGB images during the day and IR images at night, RGB-IR Re-ID is helpful when checking day and night surveillance for criminal investigations. Previous related work often only extracts sharable and identity-related features in images for identification. Few researches specifically extract and make use of features that do not have the ability to distinguish identity, e.g. identity-unrelated features derived from background and modality. In this Letter, we propose a novel and concise RGB-IR Re-ID network named two-way constraint network (TWCN). Compared with traditional Re-ID networks, TWCN not only extracts and utilises identity-related features but also novelly makes full use of identity-unrelated features to improve the accuracy of the experiment. TWCN uses a reverse-triplet loss to extract identity-unrelated features, and proposes an orthogonal constraint to remove identity-unrelated information from identity-related features, which improves the purity of identity-related features. In addition, a correlation coefficient synergy and central clustering (CCSCC) loss is introduced into TWCN to extract identity-related features effectively. Extensive experiments have been conducted to prove our method is effective.