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This is our project page. Here, we highlight current projects, both funded and unfunded. You can check out our past projects by clicking the link below.

Face Verification with Caricatures

A good caricature looks "more like a face than the face itself" -- Brennan, 1985

Caricatures exaggerate what makes someone unique, highlighting what makes them different from the average person. Humans are able to more quickly identify caricatured faces over realistic faces. We aim to combine research in machine learning and human perception in order to fundamentally change the future of face verification to inform both automated and human-based systems.




National Science Foundation

#1909707, $435K

Original grant awarded 08/2019. REU Supplement awarded 10/2020.

This material is based upon work supported by the National Science Foundation under Grant No. 1909707.


Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


Davis, Sara R., Bryson Lingenfelter, KevinMcElhinney, Shamik Sengupta, and Emily M. Hand. “CarVer: Setting the Standard for Face Verification with Caricatures.” 2023, IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2023.


Thom, Nathan, Andrew DeBolt, Lyssie Brown, and Emily M. Hand. “DoppelVer: A Benchmark for Face Verification.” 2023, Springer International Symposium on Visual Computing (ISVC). Springer, 2023.


Lingenfelter, Bryson, Sara R. Davis, and Emily M. Hand. "A Quantitative Analysis of Labeling Issues in the CelebA Dataset." International Symposium on Visual Computing. Cham: Springer International Publishing, 2022.


Davis, Sara R., and Emily M. Hand. "Improving Face Recognition Using Artistic Interpretations of Prominent Features: Leveraging Caricatures in Modern Surveillance Systems." Intelligent Video Surveillance-New Perspectives. IntechOpen, 2022.


Lingenfelter, Bryson, and Emily M. Hand. "Improving evaluation of facial attribute prediction models." 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). IEEE, 2021.


Thom, Nathan, Hung Nguyen, and Emily M. Hand. "Consensus Subspace Clustering." 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021.

Datasets & Code


ISVC, 2023

The DoppelVer dataset is a benchmark dataset designed for evaluating and advancing face verification and recognition algorithms. This dataset contains a diverse collection of facial images. The identities depicted were selected in pairs of doppelganger identities. A doppelganger is an identity who is highly visually similar to the source identity, such that they might be mistaken for one another.

Dataset: You can request the dataset by completing and submitting the End User License Agreement.


IJCB, 2023

The CarVer dataset is a benchmark dataset designed to set the standard for face verification using caricatures and veridical images. This dataset contains a diverse collection identities with caricature and real images for each identity. 

Dataset: You can request the dataset by completing and submitting the End User License Agreement.

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