# Enhancing Person Re-identification in a Self-trained Subspace

## TL;DR

This paper introduces a self-trained subspace learning method for person re-identification that leverages both labeled and unlabeled data, improving matching accuracy with fewer labeled samples.

## Contribution

It proposes a novel self-training framework with pseudo pairwise relationships and multi-kernel embedding to enhance re-ID performance using limited labeled data.

## Key findings

- Achieves comparable results to fully-supervised methods with less labeled data.
- Effectively utilizes unlabeled data through pseudo pairwise relationships.
- Improves re-ID accuracy on six benchmark datasets.

## Abstract

Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully-supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully-supervised re-ID is the limited availability of labeled training samples. To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. The proposed approach first constructs pseudo pairwise relationships among unlabeled persons using the k-nearest neighbors algorithm. Then, with the pseudo pairwise relationships, the unlabeled samples can be easily combined with the labeled samples to learn a discriminative projection by solving an eigenvalue problem. In addition, we refine the pseudo pairwise relationships iteratively, which further improves the learning performance. A multi-kernel embedding strategy is also incorporated into the proposed approach to cope with the non-linearity in person's appearance and explore the complementation of multiple kernels. In this way, the performance of person re-ID can be greatly enhanced when training data are insufficient. Experimental results on six widely-used datasets demonstrate the effectiveness of our approach and its performance can be comparable to the reported results of most state-of-the-art fully-supervised methods while using much fewer labeled data.