  As a prevailing task in video surveillance and forensics field, person
re-identification (re-ID) aims to match person images captured from
non-overlapped cameras. In unconstrained scenarios, person images often suffer
from the resolution mismatch problem, i.e., \emph{Cross-Resolution Person
Re-ID}. To overcome this problem, most existing methods restore low resolution
(LR) images to high resolution (HR) by super-resolution (SR). However, they
only focus on the HR feature extraction and ignore the valid information from
original LR images. In this work, we explore the influence of resolutions on
feature extraction and develop a novel method for cross-resolution person re-ID
called \emph{\textbf{M}ulti-Resolution \textbf{R}epresentations \textbf{J}oint
\textbf{L}earning} (\textbf{MRJL}). Our method consists of a Resolution
Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN
uses an input image to construct a HR version and a LR version with an encoder
and two decoders, while the DFFN adopts a dual-branch structure to generate
person representations from multi-resolution images. Comprehensive experiments
on five benchmarks verify the superiority of the proposed MRJL over the
relevent state-of-the-art methods.

Computer Vision and Pattern Recognition