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

TL;DR

This paper introduces a novel person re-identification system that combines tensor feature representation, multilinear subspace learning, and deep CNN features, demonstrating improved discriminability across multiple datasets.

Contribution

It proposes a new PRe-ID framework integrating tensor-based features, multilinear subspace learning, and deep CNN features for enhanced person identification accuracy.

Findings

Effective discrimination between individuals across camera views.

Improved performance on VIPeR, GRID, and PRID450s datasets.

Combines deep features with tensor and multilinear analysis for robust re-identification.

Abstract

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.