# Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning

arXiv: https://arxiv.org/abs/2312.16226 · HTML: https://mlsanity.com/paper/2312.16226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 68 references that are in the map.
- First posted on arXiv 24 Dec 2023.

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

- [Person Re-Identification by Camera Correlation Aware Feature Augmentation](https://mlsanity.com/paper/1703.08837/) (2017)
- [Deep Attributes Driven Multi-Camera Person Re-identification](https://mlsanity.com/paper/1605.03259/) (2016)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification](https://mlsanity.com/paper/1606.01595/) (2016)
- [Improving CNN-based Person Re-identification using score Normalization](https://mlsanity.com/paper/2307.00397/) (2023)
- [Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification](https://mlsanity.com/paper/1611.06969/) (2016)
- [TensorBeat: Tensor Decomposition for Monitoring Multi-Person Breathing Beats with Commodity WiFi](https://mlsanity.com/paper/1702.02046/) (2017)
- [Multi-view Deep Features for Robust Facial Kinship Verification](https://mlsanity.com/paper/2006.01315/) (2020)
- [An Intelligent Edge-Deployable Indoor Air Quality Monitoring and Activity Recognition Approach](https://mlsanity.com/paper/2311.03920/) (2023)
- [Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features](https://mlsanity.com/paper/2312.03562/) (2023)

## Built on by (most cited first, 1 of 1)

- [Dissecting Human Body Representations in Deep Networks Trained for Person Identification](https://mlsanity.com/paper/2502.15934/) (2025)

## Abstract (arXiv)

Person re-identification (PRe-ID) is a computer vision issue, that has been a fertile research area in the last few years. It aims to identify persons across different non-overlapping camera views. In this paper, We propose a novel PRe-ID system that combines tensor feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Convolutional Neural Networks (CNNs) as a strong deep feature extractor, along with two complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis (TXQDA) is used to learn a discriminative subspace that enhances the separability between different individuals. Mahalanobis distance is used to match and similarity computation between query and gallery samples. Finally, we evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s.