# Recognizing Partial Biometric Patterns

arXiv: https://arxiv.org/abs/1810.07399 · HTML: https://mlsanity.com/paper/1810.07399/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 22 of 22)

- [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021)
- [AAformer: Auto-Aligned Transformer for Person Re-Identification](https://mlsanity.com/paper/2104.00921/) (2021)
- [Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2107.02380/) (2021)
- [Feature Erasing and Diffusion Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2112.08740/) (2021)
- [Feature Completion for Occluded Person Re-Identification](https://mlsanity.com/paper/2106.12733/) (2021)
- [Region Generation and Assessment Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2309.03558/) (2023)
- [Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification](https://mlsanity.com/paper/2108.00139/) (2021)
- [Quality-aware Part Models for Occluded Person Re-identification](https://mlsanity.com/paper/2201.00107/) (2022)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval](https://mlsanity.com/paper/2210.13440/) (2022)
- [Holistic Guidance for Occluded Person Re-Identification](https://mlsanity.com/paper/2104.06524/) (2021)
- [Feature Completion Transformer for Occluded Person Re-identification](https://mlsanity.com/paper/2303.01656/) (2023)
- [Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification](https://mlsanity.com/paper/2105.07345/) (2021)
- [Short Range Correlation Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2201.01090/) (2022)
- [Motion-Aware Transformer For Occluded Person Re-identification](https://mlsanity.com/paper/2202.04243/) (2022)
- [Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification](https://mlsanity.com/paper/2402.10435/) (2024)
- [Deep learning for identification and face, gender, expression recognition under constraints](https://mlsanity.com/paper/2111.01930/) (2021)
- [Dynamic Template Initialization for Part-Aware Person Re-ID](https://mlsanity.com/paper/2208.11440/) (2022)
- [Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification](https://mlsanity.com/paper/2308.15063/) (2023)
- [DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification](https://mlsanity.com/paper/2401.18032/) (2024)
- [DDRN:a Data Distribution Reconstruction Network for Occluded Person Re-Identification](https://mlsanity.com/paper/2410.06600/) (2024)
- [Cross-Modal Synergies: Unveiling the Potential of Motion-Aware Fusion Networks in Handling Dynamic and Static ReID Scenarios](https://mlsanity.com/paper/2502.00665/) (2025)

## Abstract (arXiv)

Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching. In this area, deep learning based methods are widely applied to match these partial captured objects caused by occlusions, variations of postures or just partial out of view in person re-identification and partial face recognition. However, most current methods are not able to identify an individual in case that some parts of the object are not obtainable, while the rest are specialized to certain constrained scenarios. To this end, we propose a robust general framework for arbitrary biometric matching scenarios without the limitations of alignment as well as the size of inputs. We introduce a feature post-processing step to handle the feature maps from FCN and a dictionary learning based Spatial Feature Reconstruction (SFR) to match different sized feature maps in this work. Moreover, the batch hard triplet loss function is applied to optimize the model. The applicability and effectiveness of the proposed method are demonstrated by the results from experiments on three person re-identification datasets (Market1501, CUHK03, DukeMTMC-reID), two partial person datasets (Partial REID and Partial iLIDS) and two partial face datasets (CASIA-NIR-Distance and Partial LFW), on which state-of-the-art performance is ensured in comparison with several state-of-the-art approaches. The code is released online and can be found on the website: https://github.com/lingxiao-he/Partial-Person-ReID.