# Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification

arXiv: https://arxiv.org/abs/1807.10908 · HTML: https://mlsanity.com/paper/1807.10908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [ASMR: Learning Attribute-Based Person Search with Adaptive Semantic Margin Regularizer](https://mlsanity.com/paper/2108.04533/) (2021)
- [Attribute Guided Sparse Tensor-Based Model for Person Re-Identification](https://mlsanity.com/paper/2108.04352/) (2021)

## Abstract (arXiv)

In this paper we propose a novel metric learning framework called Nullspace Kernel Maximum Margin Metric Learning (NK3ML) which efficiently addresses the small sample size (SSS) problem inherent in person re-identification and offers a significant performance gain over existing state-of-the-art methods. Taking advantage of the very high dimensionality of the feature space, the metric is learned using a maximum margin criterion (MMC) over a discriminative nullspace where all training sample points of a given class map onto a single point, minimizing the within class scatter. A kernel version of MMC is used to obtain a better between class separation. Extensive experiments on four challenging benchmark datasets for person re-identification demonstrate that the proposed algorithm outperforms all existing methods. We obtain 99.8% rank-1 accuracy on the most widely accepted and challenging dataset VIPeR, compared to the previous state of the art being only 63.92%.