Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification

TL;DR

This paper introduces a novel metric learning framework using Kernel Fisher Discriminant Analysis with multiple kernels to improve person re-identification accuracy across different camera views.

Contribution

It proposes a new Mahalanobis metric derived from KFDA and enhances its efficiency with multiple kernel learning methods for better person re-identification.

Findings

Achieves competitive performance on benchmark datasets

Demonstrates the effectiveness of multiple kernel learning in metric enhancement

Validates the proposed method's superiority over some existing approaches

Abstract

Person re-identification addresses the problem of matching pedestrian images across disjoint camera views. Design of feature descriptor and distance metric learning are the two fundamental tasks in person re-identification. In this paper, we propose a metric learning framework for person re-identification, where the discriminative metric space is learned using Kernel Fisher Discriminant Analysis (KFDA), to simultaneously maximize the inter-class variance as well as minimize the intra-class variance. We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification. We also show how the efficiency of KFDA in metric learning can be further enhanced for person re-identification by using two simple yet efficient multiple kernel learning methods. We conduct extensive experiments on three benchmark datasets for person re-identification and demonstrate that the proposed approaches have competitive performance with state-of-the-art methods.