Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification

TL;DR

This paper presents a multi-feature Kernel Canonical Correlation Analysis approach to improve cross-view person re-identification by maximizing appearance correlation across camera views, demonstrating competitive results on standard datasets.

Contribution

Introduces a multi-feature KCCA method with iterative feature weighting for enhanced cross-view person re-identification performance.

Findings

Achieves comparable results on VIPeR and PRID 450s datasets.

Outperforms state-of-the-art on PRID and CUHK01 datasets.

Effectively models appearance changes across camera views.

Abstract

In this paper we introduce a method to overcome one of the main challenges of person re-identification in multi-camera networks, namely cross-view appearance changes. The proposed solution addresses the extreme variability of person appearance in different camera views by exploiting multiple feature representations. For each feature, Kernel Canonical Correlation Analysis (KCCA) with different kernels is exploited to learn several projection spaces in which the appearance correlation between samples of the same person observed from different cameras is maximized. An iterative logistic regression is finally used to select and weigh the contributions of each feature projections and perform the matching between the two views. Experimental evaluation shows that the proposed solution obtains comparable performance on VIPeR and PRID 450s datasets and improves on PRID and CUHK01 datasets with respect to the state of the art.