Clothing-Change Feature Augmentation for Person Re-Identification

Clothing-change person re-identification (CC Re-ID) aims to match the same person who changes clothes across cameras. Current methods are usually limited by the insufficient number and variation of clothing in training data, e.g. each person only has 2 outfits in the PRCC dataset. In this work, we propose a novel Clothing-Change Feature Augmentation (CCFA) model for CC Re-ID to largely expand clothing-change data in the feature space rather than visual image space. It automatically models the feature distribution expansion that reflects a person's clothing colour and texture variations to augment model training. Specifically, to formulate meaningful clothing variations in the feature space, our method first estimates a clothing-change normal distribution with intra-ID cross-clothing variances. Then an augmentation generator learns to follow the estimated distribution to augment plausible clothing-change features. The augmented features are guaranteed to maximise the change of clothing and minimise the change of identity properties by adversarial learning to assure the effectiveness. Such augmentation is performed iteratively with an ID-correlated augmentation strategy to increase intra-ID clothing variations and reduce inter-ID clothing variations, enforcing the Re-ID model to learn clothing-independent features inherently. Extensive experiments demonstrate the effectiveness of our method with state-of-the-art results on CC Re-ID datasets.