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Local Connectivity-Based Density Estimation for Face Clustering

This repo contains an official implementation for CVPR'23 paper "Local Connectivity-Based Density Estimation for Face Clustering".

Introduction

The proposed clustering method employs density-based clustering, which maintains edges that have higher density. For this purpose, we propose a reliable density estimation algorithm based on local connectivity between K nearest neighbors (KNN). We effectively exclude negative pairs from the KNN graph based on the reliable density while maintaining sufficient positive pairs. Furthermore, we develop a pairwise connectivity estimation network to predict the connectivity of the selected edges.

Requirements

• Python = 3.8.5
• Pytorch = 1.10.2

conda install pytorch==1.10.2 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Dataset

The data directory is constructed as follows:

data
  ├── ms1m
  |    ├── features
  |    |    ├── part1_test.bin
  |    |    ├── ...
  |    |    └── part9.test.bin
  |    ├── labels
  |    |    ├── part1_test.meta
  |    |    ├── ...
  |    |    └── part9.test.meta
  |    └── knns
  |         ├── part1_test
  |         |    └── faiss_k_80.npz
  |         ├── ...
  |         └── part9_test
  |              └── faiss_k_80.npz
  ├── deepfashion
  |    ├── features
  |    |    └── deepfashion_test.bin
  |    ├── labels
  |    |    └── deepfashion_test.meta
  |    └── knns
  |         └── deepfashion_test
  |              └── deepfashion_k40.npz
  └── ijb-b
       ├── 512.fea.npy
       ├── 512.labels.npy
       ├── knn.graph.512.bf.npy
       └── ...

We have used the data from following repositories.

• [MS-Celeb-1M, DeepFashion] https://github.com/yl-1993/learn-to-cluster
• [CASIA/IJB-B] https://github.com/Zhongdao/gcn_clustering

For DeepFashion dataset, we construct kNN graph using faiss.

• Google Drive

Run

To test for each dataset, simply run shell scripts.

sh inference_{dataset_name}.sh

Results on MS-Celeb-1M

1, 3, 5, 7, 9 mean different subset of the clustering benchmark. Detailed settings are on our paper.

Pairwise F-Score

BCubed F-Score

Results on IJB-B

$F_{512}, F_{1024}, F_{1845}$ mean different subset of the clustering benchmark. Detailed settings are on our paper.

Pairwise F-Score

BCubed F-Score

Results on DeepFashion

Acknowledgement

Some codes are based on the publicly available codebase https://github.com/yl-1993/learn-to-cluster.

Citation

@inproceedings{shin2023local,
  title={Local Connectivity-Based Density Estimation for Face Clustering},
  author={Shin, Junho and Lee, Hyo-Jun and Kim, Hyunseop and Baek, Jong-Hyeon and Kim, Daehyun and Koh, Yeong Jun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023}
}