# Person re-identification with fusion of hand-crafted and deep pose-based body region features

arXiv: https://arxiv.org/abs/1803.10630 · HTML: https://mlsanity.com/paper/1803.10630/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Pose Invariant Person Re-Identification using Robust Pose-transformation GAN](https://mlsanity.com/paper/2105.00930/) (2021)

## Abstract (arXiv)

Person re-identification (re-ID) aims to accurately re- trieve a person from a large-scale database of images cap- tured across multiple cameras. Existing works learn deep representations using a large training subset of unique per- sons. However, identifying unseen persons is critical for a good re-ID algorithm. Moreover, the misalignment be- tween person crops to detection errors or pose variations leads to poor feature matching. In this work, we present a fusion of handcrafted features and deep feature representa- tion learned using multiple body parts to complement the global body features that achieves high performance on un- seen test images. Pose information is used to detect body regions that are passed through Convolutional Neural Net- works (CNN) to guide feature learning. Finally, a metric learning step enables robust distance matching on a dis- criminative subspace. Experimental results on 4 popular re-ID benchmark datasets namely VIPer, DukeMTMC-reID, Market-1501 and CUHK03 show that the proposed method achieves state-of-the-art performance in image-based per- son re-identification.