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. 2023 Jul 14;13:11396. doi: 10.1038/s41598-023-28632-x

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Optimal accuracy in face recognition is achieved by aggregating responses of diverse experts. Violin plots show the distribution of accuracy scores (AUCs) for each fusion. Red lines show median accuracy. Comparison of individuals, pairs and triplets shows increased accuracy with increasing group size. The best results occur when fusing responses from both humans and DNNs (yellow), resulting in more accurate decisions compared to either human–human (purple) or DNN-DNN fusions. Fusing human experts’ decisions models decisions made by forensic laboratories, and the benefits of doing so may explain the superiority of forensic laboratory decisions. Here, we report the best performing DNN (DNN10) for the individuals analysis, and the DNNs that produce the strongest fusion effects with DNN10 for the pairs (DNN3) and triplets (DNN3 and DNN1) analyses. However, we note the results are consistent for almost all DNNS. SR = super-recognizer; EX = forensic examiner.