Deep Spatial Feature Reconstruction for Partial Person Re-identification:
Alignment-free Approach
Abstract
Partial person re-identification (re-id) is a challenging
problem, where only several partial observations (images)
of people are available for matching. However, few studies have provided flexible solutions to identifying a person
in an image containing arbitrary part of the body. In this
paper, we propose a fast and accurate matching method to
address this problem. The proposed method leverages Fully
Convolutional Network (FCN) to generate fix-sized spatial
feature maps such that pixel-level features are consistent.
To match a pair of person images of different sizes, a novel
method called Deep Spatial feature Reconstruction (DSR)
is further developed to avoid explicit alignment. Specifi-
cally, DSR exploits the reconstructing error from popular
dictionary learning models to calculate the similarity between different spatial feature maps. In that way, we expect
that the proposed FCN can decrease the similarity of coupled images from different persons and increase that from
the same person. Experimental results on two partial person datasets demonstrate the efficiency and effectiveness of
the proposed method in comparison with several state-ofthe-art partial person re-id approaches. Additionally, DSR
achieves competitive results on a benchmark person dataset
Market1501 with 83.58% Rank-1 accuracy