Learning comprehensive global features in person re-identification: Ensuring discriminativeness of more local regions

•A novel baseline for person re-identification is proposed to learn comprehensive global embedding, ensuring that more local regions (the number of local regions is manually defined) of global feature maps are discriminative.•A Non-parameterized Local Classifier (NLC) module is designed to obtain a score vector of each local region on feature maps in a non-parametric manner.•A Comprehensive Global Embedding (CGE) module is designed to revise the global logits such that the subsequent cross entropy loss up-weights the loss assigned to samples with hard-to-learn local regions.•The network achieves 65.9% mAP, 85.1% rank1 on MSMT17, 86.4% mAP, 87.4% rank1 on CUHK03 labeled, 84.2% mAP, 85.9% rank1 on CUHK03 detected, and 92.2% mAP, 96.3% rank1 on Market-1501.