Cloth-changing person re-identification paradigm based on domain augmentation and adaptation

In order to solve the influence of the clothing change on the model' s recognition accuracy of the personal identity, a clothes-changing person re-identification paradigm based on domain augmentation and adaptation is proposed, which enables the model to learn general robust identity representation features in different domains. First, a clothing semantic-aware domain data enhancement method is designed based on the semantic information of the human body, which changes the color of sample clothes without changing the identity of the target person to fill the lack of domain diversity in the data; second, a multi-positive class domain adaptive loss function is designed, which assigns differential weights to the multi-positive class data losses according to the different contributions made by different domain data in the model training, forcing the model to focus on the learning of generic identity features of the samples. Experiments demonstrate that the method achieves 59. 5%, 60. 0%, and 88. 0%, 84. 5% of Rank-1 and mAP on two clothing change datasets, PRCC and CCVID, without affecting the accuracy of non-clothing person re-identification. Compared with other methods, this method has a higher accuracy and stronger robustness and significantly improves the model's ability to recognize persons. © 2023 Science Press. All rights reserved.