# Pore-scale Image Patch Dataset and A Comparative Evaluation of Pore-scale Facial Features

arXiv: https://arxiv.org/abs/2512.00381 · HTML: https://mlsanity.com/paper/2512.00381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads](https://mlsanity.com/paper/2305.03027/) (2023)
- [SOSNet: Second Order Similarity Regularization for Local Descriptor Learning](https://mlsanity.com/paper/1904.05019/) (2019)
- [A Decade Survey of Content Based Image Retrieval using Deep Learning](https://mlsanity.com/paper/2012.00641/) (2020)
- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT](https://mlsanity.com/paper/1405.5769/) (2014)
- [SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning](https://mlsanity.com/paper/2106.04434/) (2021)
- [Robust Angular Local Descriptor Learning](https://mlsanity.com/paper/1901.07076/) (2019)
- [A Large Dataset for Improving Patch Matching](https://mlsanity.com/paper/1801.01466/) (2018)

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

- [PoreTrack3D: A Benchmark for Dynamic 3D Gaussian Splatting in Pore-Scale Facial Trajectory Tracking](https://mlsanity.com/paper/2512.02648/) (2025)

## Abstract (arXiv)

The weak-texture nature of facial skin regions presents significant challenges for local descriptor matching in applications such as facial motion analysis and 3D face reconstruction. Although deep learning-based descriptors have demonstrated superior performance to traditional hand-crafted descriptors in many applications, the scarcity of pore-scale image patch datasets has hindered their further development in the facial domain. In this paper, we propose the PorePatch dataset, a high-quality pore-scale image patch dataset, and establish a rational evaluation benchmark. We introduce a Data-Model Co-Evolution (DMCE) framework to generate a progressively refined, high-quality dataset from high-resolution facial images. We then train existing SOTA models on our dataset and conduct extensive experiments. Our results show that the SOTA model achieves a FPR95 value of 1.91% on the matching task, outperforming PSIFT (22.41%) by a margin of 20.5%. However, its advantage is diminished in the 3D reconstruction task, where its overall performance is not significantly better than that of traditional descriptors. This indicates that deep learning descriptors still have limitations in addressing the challenges of facial weak-texture regions, and much work remains to be done in this field.