# Face Clustering for Connection Discovery from Event Images

arXiv: https://arxiv.org/abs/2309.01092 · HTML: https://mlsanity.com/paper/2309.01092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis](https://mlsanity.com/paper/1704.04086/) (2017)
- [Linkage Based Face Clustering via Graph Convolution Network](https://mlsanity.com/paper/1903.11306/) (2019)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [Learning Residual Images for Face Attribute Manipulation](https://mlsanity.com/paper/1612.05363/) (2016)
- [Faceless Person Recognition; Privacy Implications in Social Media](https://mlsanity.com/paper/1607.08438/) (2016)
- [Clustering Millions of Faces by Identity](https://mlsanity.com/paper/1604.00989/) (2016)
- [Dual-Glance Model for Deciphering Social Relationships](https://mlsanity.com/paper/1708.00634/) (2017)
- [Clustering based Contrastive Learning for Improving Face Representations](https://mlsanity.com/paper/2004.02195/) (2020)
- [A Domain Based Approach to Social Relation Recognition](https://mlsanity.com/paper/1704.06456/) (2017)
- [Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment](https://mlsanity.com/paper/1812.01936/) (2018)
- [StreetStyle: Exploring world-wide clothing styles from millions of photos](https://mlsanity.com/paper/1706.01869/) (2017)
- [Face Clustering: Representation and Pairwise Constraints](https://mlsanity.com/paper/1706.05067/) (2017)
- [A Comprehensive Survey on Pose-Invariant Face Recognition](https://mlsanity.com/paper/1502.04383/) (2015)
- [Learn to Cluster Faces via Pairwise Classification](https://mlsanity.com/paper/2205.13117/) (2022)
- [Efficient Large-Scale Face Clustering Using an Online Mixture of Gaussians](https://mlsanity.com/paper/2103.17272/) (2021)
- [Face frontalization for Alignment and Recognition](https://mlsanity.com/paper/1502.00852/) (2015)

## Abstract (arXiv)

Social graphs are very useful for many applications, such as recommendations and community detections. However, they are only accessible to big social network operators due to both data availability and privacy concerns. Event images also capture the interactions among the participants, from which social connections can be discovered to form a social graph. Unlike online social graphs, social connections carried by event images can be extracted without user inputs, and hence many social graph-based applications become possible, even without access to online social graphs. This paper proposes a system to discover social connections from event images. By utilizing the social information from even images, such as co-occurrence, a face clustering method is proposed and implemented, and connections can be discovered without the identity of the event participants. By collecting over 40000 faces from over 3000 participants, it is shown that the faces can be well clustered with 80% in F1 score, and social graphs can be constructed. Utilizing offline event images may create a long-term impact on social network analytics.