More Examples, More Competitors: The Reference-Photo Budget in Closed-Set Face Matching
Abstract
Adding reference photographs helps a nearest-gallery matcher see more of each person, but also gives competing identities more opportunities to look similar. On one fixed Olivetti split, increasing the gallery from one to three and five photographs per identity raised correctness from 56.0% to 75.5% and 85.5%. The five-photo policy corrected sixty formerly wrong queries and made one formerly correct query wrong. This transparent pixel-based experiment measures the value of a reference budget within a known roster. It does not estimate the number of images needed by a learned identity-preserving generator.
Public-data research draft. Conditional benchmark analysis; final scientific and publication review is pending.
Original publication: October 24, 2026.
References
- Deng, Jiankang, et al. "ArcFace: Additive Angular Margin Loss for Deep Face Recognition" arXiv preprint arXiv:1801.07698, 2018. https://arxiv.org/abs/1801.07698.
- Meng, Qiang, et al. "MagFace: A Universal Representation for Face Recognition and Quality Assessment." arXiv preprint arXiv:2103.06627, 2021. https://arxiv.org/abs/2103.06627.
- Kim, Minchul, et al. "AdaFace: Quality Adaptive Margin for Face Recognition." arXiv preprint arXiv:2204.00964, 2022. https://arxiv.org/abs/2204.00964.
- Gebru, Timnit, et al. "Datasheets for datasets" Communications of the ACM, 2021. https://doi.org/10.1145/3458723.
- Wang, Hao, et al. "CosFace: Large Margin Cosine Loss for Deep Face Recognition" arXiv preprint arXiv:1801.09414, 2018. https://arxiv.org/abs/1801.09414.
- Kemelmacher-Shlizerman, Ira, et al. "The MegaFace Benchmark: 1 Million Faces for Recognition at Scale" arXiv preprint arXiv:1512.00596, 2015. https://arxiv.org/abs/1512.00596.
- White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817-838. https://doi.org/10.2307/1912934.
- Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317-372. https://doi.org/10.3368/jhr.50.2.317.
- Roberts, David R., et al. “Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure.” Ecography, vol. 40, no. 8, 2017, pp. 913-929. https://doi.org/10.1111/ecog.02881.
- Meng, Zijie; Liu, Jiwen; Liu, Yufei; Tong, Chengzhuo; Liu, Xiaoqiang; Zhang, Yuanxing; Xu, Yulong; Wan, Pengfei. (2026). ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation. arXiv:2606.11670. https://arxiv.org/abs/2606.11670.
- Li, Zhen, et al. "PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding." arXiv preprint arXiv:2312.04461, 2023. https://arxiv.org/abs/2312.04461.
- Kärkkäinen, Kimmo and Jungseock Joo. "FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age" arXiv preprint arXiv:1908.04913, 2019. https://arxiv.org/abs/1908.04913.
- Turk, M.A.; Pentland, A.P.. (1991). Face recognition using eigenfaces. Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1991.139758.
- Huang, Gary B., Marwan Mattar, Tamara Berg, and Erik Learned-Miller. "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments." ECCV Workshop on Faces in Real-Life Images, 2008. https://cs.umass.edu/~elm/papers/Huang_eccv2008-lfw.pdf.
- Gneiting, Tilmann; Raftery, Adrian E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. https://doi.org/10.1198/016214506000001437.
- Belhumeur, P.N.; Hespanha, J.P.; Kriegman, D.J.. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.598228.
- Wang, Qixun, et al. "InstantID: Zero-shot Identity-Preserving Generation in Seconds." arXiv preprint arXiv:2401.07519, 2024. https://arxiv.org/abs/2401.07519.
- Yuan, Shenghai; Huang, Jinfa; He, Xianyi; Ge, Yunyuan; Shi, Yujun; Chen, Liuhan; Luo, Jiebo; Yuan, Li. (2024). Identity-Preserving Text-to-Video Generation by Frequency Decomposition. arXiv:2411.17440. https://arxiv.org/abs/2411.17440.
- Fawcett, Tom. (2006). An introduction to ROC analysis. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2005.10.010.
- Samaria, F.S.; Harter, A.C.. (1994). Parameterisation of a stochastic model for human face identification. Proceedings of 1994 IEEE Workshop on Applications of Computer Vision. https://doi.org/10.1109/acv.1994.341300.
- AT&T Laboratories Cambridge. (2026). Modified Olivetti faces: Sam Roweis MATLAB version [Data archive; mirror accessed 7 November 2026]. File identity and row-label layout verified against the scikit-learn maintainer loader. https://ndownloader.figshare.com/files/5976027.
- Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017. https://arxiv.org/abs/1705.08500.
- Guo, Zinan, et al. "PuLID: Pure and Lightning ID Customization via Contrastive Alignment." arXiv preprint arXiv:2404.16022, 2024. https://arxiv.org/abs/2404.16022.
- He, Xuanhua; Liu, Quande; Qian, Shengju; Wang, Xin; Hu, Tao; Cao, Ke; Yan, Keyu; Zhang, Jie. (2024). ID-Animator: Zero-Shot Identity-Preserving Human Video Generation. arXiv:2404.15275. https://arxiv.org/abs/2404.15275.
- Cao, Qiong, et al. "VGGFace2: A dataset for recognising faces across pose and age" arXiv preprint arXiv:1710.08092, 2017. https://arxiv.org/abs/1710.08092.
- Cook, R. Dennis. (1977). Detection of Influential Observation in Linear Regression. Technometrics. https://doi.org/10.1080/00401706.1977.10489493.
- Varma, Sudhir and Richard Simon. “Bias in error estimation when using cross-validation for model selection.” BMC Bioinformatics, vol. 7, no. 1, 2006. https://doi.org/10.1186/1471-2105-7-91.
- Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; Axton, Myles; Baak, Arie; Blomberg, Niklas; Boiten, Jan-Willem; da Silva Santos, Luiz Bonino; Bourne, Philip E.; Bouwman, Jildau; Brookes, Anthony J.; Clark, Tim; Crosas, Mercè; Dillo, Ingrid; Dumon, Olivier; Edmunds, Scott; Evelo, Chris T.; Finkers, Richard; Gonzalez-Beltran, Alejandra; Gray, Alasdair J.G.; Groth, Paul; Goble, Carole; Grethe, Jeffrey S.; Heringa, Jaap; ’t Hoen, Peter A.C; Hooft, Rob; Kuhn, Tobias; Kok, Ruben; Kok, Joost; Lusher, Scott J.; Martone, Maryann E.; Mons, Albert; Packer, Abel L.; Persson, Bengt; Rocca-Serra, Philippe; Roos, Marco; van Schaik, Rene; Sansone, Susanna-Assunta; Schultes, Erik; Sengstag, Thierry; Slater, Ted; Strawn, George; Swertz, Morris A.; Thompson, Mark; van der Lei, Johan; van Mulligen, Erik; Velterop, Jan; Waagmeester, Andra; Wittenburg, Peter; Wolstencroft, Katherine; Zhao, Jun; Mons, Barend. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. https://doi.org/10.1038/sdata.2016.18.
- Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019. https://arxiv.org/abs/1906.02530.
- Schroff, Florian, et al. "FaceNet: A Unified Embedding for Face Recognition and Clustering" arXiv preprint arXiv:1503.03832, 2015. https://arxiv.org/abs/1503.03832.
Published
Issue
Section
License
Copyright (c) 2026 George Wells, Henry Collins, Ryan King

This work is licensed under a Creative Commons Attribution 4.0 International License.