# Revisiting Face Recognition for Monozygotic Twins: The Celeb Twins Test Set

arXiv: https://arxiv.org/abs/2609.01141 · HTML: https://mlsanity.com/paper/2609.01141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Synthetic Data for Face Recognition: Current State and Future Prospects](https://mlsanity.com/paper/2305.01021/) (2023)
- [Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors](https://mlsanity.com/paper/2409.02979/) (2024)
- [The Do's and Don'ts for CNN-based Face Verification](https://mlsanity.com/paper/1705.07426/) (2017)
- [The Criminality From Face Illusion](https://mlsanity.com/paper/2006.03895/) (2020)
- [Can the accuracy bias by facial hairstyle be reduced through balancing the training data?](https://mlsanity.com/paper/2405.20062/) (2024)
- [Twin identification over viewpoint change: A deep convolutional neural network surpasses humans](https://mlsanity.com/paper/2207.05316/) (2022)
- [Goldilocks Test Sets for Face Verification](https://mlsanity.com/paper/2405.15965/) (2024)
- [Impact of Sunglasses on One-to-Many Facial Identification Accuracy](https://mlsanity.com/paper/2412.05721/) (2024)
- [Benchmarking Human Face Similarity Using Identical Twins](https://mlsanity.com/paper/2208.11822/) (2022)

## Abstract (arXiv)

Past literature on face recognition for monozygotic (("identical") twins points to facial marks and mirror asymmetry as possible directions for improved accuracy of twins recognition. The Celeb Twins Test Set (CTTS) contains web-scraped image pairs for 80 sets of celebrity twins. It is the only twins test set with meta-data for twins with distinguishing skin marks and possible mirror asymmetry. CTTS is organized in the manner of face verification test sets such as LFW, CALFW, CPLFW, CFP-FP, and AgeDB-30. Current deep CNN matchers can achieve over 76% accuracy in classifying CTTS same-person / different-person image pairs. We show that current matchers do not make use of skin marks, or asymmetry, and discuss reasons for this. Finally, we discuss the feasibility of using generative AI tools such as Grok, ChatGPT and Gemini to create images of imagined monozygotic twins as a means to increase representation of twins in face recognition training sets.