arXiv Computer Vision By P. Jonathon Phillips (Information Access Division, National Institute of Standards and Technology, Gaithersburg, MD), Geraldine Jeckeln (School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX), Amy N. Yates (Information Access Division, National Institute of Standards and Technology, Gaithersburg, MD), Carina A. Hahn (Information Access Division, National Institute of Standards and Technology, Gaithersburg, MD), Peter C. Fontana (Information Access Division, National Institute of Standards and Technology, Gaithersburg, MD), Alice J. O'Toole (School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX)

Unlocking the power of partnership: How humans and machines can work together to improve face recognition

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arXiv AI
Jun 4

CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems

arXiv:2407. 13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential.

By Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed, Harrison J Rosenberg, Ramya Korlakai Vinayak, Kassem Fawaz
arXiv AI
Sep 2

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

The paper introduces the Celeb Twins Test Set (CTTS), a collection of web‑scraped image pairs for 80 sets of celebrity monozygotic twins, uniquely annotated with skin marks and potential mirror asymmetry. It evaluates current deep CNN matchers, noting they achieve over 76% accuracy yet fail to leverage these distinguishing features. The authors also explore using generative AI tools to synthesize twin images to enhance training data representation.

By Michael Zang, Haiyu Wu, Mrinal Sharma, Kevin W. Bowyer