Joint Color-irrelevant Consistency Learning and Identity-aware Modality Adaptation for Visible-infrared Cross Modality Person Re-identification

Visible-infrared cross modality person re-identification (VI- ReID) is a core but challenging technology in the 24-hours intelligent surveillance system. How to eliminate the large modality gap lies in the heart of VI-ReID. Conventional methods mainly focus on directly aligning the heterogeneous modalities into the same space. However, due to the unbalanced color information between the visible and infrared images, the features of visible images tend to overfit the clothing color information, which would be harmful to the modality alignment. Besides, these methods mainly align the heterogeneous feature distributions in dataset-level while ignoring the valuable identity information, which may cause the feature misalignment of some identities and weaken the discrimination of features. To tackle above problems, we propose a novel approach for VI-ReID. It learns the colorirrelevant features through the color-irrelevant consistency learning (CICL) and aligns the identity-level feature distributions by the identity-aware modality adaptation (IAMA). The CICL and IAMA are integrated into a joint learning framework and can promote each other. Extensive experiments on two popular datasets SYSU-MM01 and RegDB demonstrate the superiority and effectiveness of our approach against the state-of-the-art methods.

Authors

• Zhiwei Zhao School of Cyber Science and Technology, University of Science and Technology of China Anhui Province Key Laboratory of Digital Security the CCCD Key Lab of Ministry of Culture and Tourism
• Bin Liu
• Qi Chu School of Cyber Science and Technology, University of Science and Technology of China Anhui Province Key Laboratory of Digital Security the CCCD Key Lab of Ministry of Culture and Tourism
• Yan Lu Zhongnan University of Economics and Law
• Nenghai Yu School of Cyber Science and Technology, University of Science and Technology of China Anhui Province Key Laboratory of Digital Security the CCCD Key Lab of Ministry of Culture and Tourism

Keywords

No keywords are indexed for this paper.