Ongoing face recognition vendor test (FRVT) part 2:
By Patrick Grother, Mei Ngan, Kayee K. Hanaoka. 2018
109 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Dacheng Tao
- Andrea Vedaldi
- Jitendra Malik
- Cordelia Schmid
- Trevor Darrell
- Xiaogang Wang
In Evaluation & Benchmarks
- Vittorio Ferrari
- Morgan Klaus Scheuerman
- Aleksandr Petiushko
- Pietro Perona
- Albert Gordo
- Wei Xia
- Caroline Pantofaru
- Stepan Alekseevich Komkov
Citations in the corpus
- A Step Toward More Inclusive People Annotations for Fairness (2021, background): “for training or evaluating face recognition algorithms may include annotations for fairness analysis [11, 26].”
- How Computers See Gender (2019, background): “Knowing that facial analysis technologies are not 100% accurate [42], removing these photos allowed us to focus on gender presentation in ideal circumstances and better isolate how these services classify and label gender in each individual image.”
- Towards causal benchmarking of bias in face analysis algorithms (2020, background): “Benchmarking in computer vision has a long history [6, 9, 22] including face recognition [47, 60, 61, 62, 27, 28] and face analysis [12].”
- Demographic Effects in Facial Recognition and Their Dependence on Image Acquisition: An Evaluation of Eleven Commercial Systems (2019, methodology): “Since demographic effects can vary significantly between algorithms [8] it will be important to consider how the choice of matching algorithm affects biometric system performance across diverse demographics.”
- Towards Measuring Fairness in AI: The Casual Conversations Dataset (2021, background): “Grother et al. [21] evaluated demographic effects of the face recognition technologies on four large datasets of photos collected in U.S. governmental applications.”
- AdvHat: Real-World Adversarial Attack on ArcFace Face ID System (2019, methodology): “For example, the ArcFace (Insightface) solution is comparable with Microsoft and Google models in such challenges as Megaface [11, 12], NIST FRVT [13], and Trillion Pairs [14].”