# Improving CNN-based Person Re-identification using score Normalization

arXiv: https://arxiv.org/abs/2307.00397 · HTML: https://mlsanity.com/paper/2307.00397/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 1 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification](https://mlsanity.com/paper/1803.09786/) (2018)
- [Multi-Level Factorisation Net for Person Re-Identification](https://mlsanity.com/paper/1803.09132/) (2018)
- [Learning a Discriminative Null Space for Person Re-identification](https://mlsanity.com/paper/1603.02139/) (2016)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Panoptic Segmentation: A Review](https://mlsanity.com/paper/2111.10250/) (2021)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [An End-to-End Foreground-Aware Network for Person Re-Identification](https://mlsanity.com/paper/1910.11547/) (2019)
- [Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification](https://mlsanity.com/paper/1611.06969/) (2016)
- [Privacy-Preserving Speaker Recognition with Cohort Score Normalisation](https://mlsanity.com/paper/1907.03454/) (2019)

## Built on by (most cited first, 5 of 5)

- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)
- [Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features](https://mlsanity.com/paper/2312.03562/) (2023)
- [Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning](https://mlsanity.com/paper/2312.16226/) (2023)
- [A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses](https://mlsanity.com/paper/2312.11290/) (2023)

## Abstract (arXiv)

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S, and promising results are obtained. For example, without normalization, the rank-20 rate accuracies of the GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%, 96.22%; however, after score normalization, they have increased to 64.64%, 89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results on four challenging datasets indicate the effectiveness of the proposed approach.