# An Enhanced Deep Feature Representation for Person Re-identification

arXiv: https://arxiv.org/abs/1604.07807 · HTML: https://mlsanity.com/paper/1604.07807/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2016.

## Built on by (most cited first, 6 of 6)

- [Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification](https://mlsanity.com/paper/2303.10976/) (2023)
- [Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification](https://mlsanity.com/paper/2301.00531/) (2023)
- [Pose-Aided Video-based Person Re-Identification via Recurrent Graph Convolutional Network](https://mlsanity.com/paper/2209.11582/) (2022)
- [Multi-Granularity Graph Pooling for Video-based Person Re-Identification](https://mlsanity.com/paper/2209.11584/) (2022)
- [Markerless retro-identification complements re-identification of individual insect subjects in archived image data of biological experiments](https://mlsanity.com/paper/2405.13376/) (2024)
- [Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology](https://mlsanity.com/paper/2506.04143/) (2025)

## Abstract (arXiv)

Feature representation and metric learning are two critical components in person re-identification models. In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural Network (CNN) features. We propose a novel feature extraction model called Feature Fusion Net (FFN) for pedestrian image representation. In FFN, back propagation makes CNN features constrained by the handcrafted features. Utilizing color histogram features (RGB, HSV, YCbCr, Lab and YIQ) and texture features (multi-scale and multi-orientation Gabor features), we get a new deep feature representation that is more discriminative and compact. Experiments on three challenging datasets (VIPeR, CUHK01, PRID450s) validates the effectiveness of our proposal.