Factors that influence algorithm performance in the Face Recognition Grand Challenge

100 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “the early 2000s, focusing primarily on performance for groups based on environmental factors like pose and lighting conditions (e.g., Lee, Ho, and Kriegman 2005; Beveridge et al. 2009; Klare et al. 2012) and for groups based on gender, age, and race (e.”
FRVT 2006: Quo Vadis face quality (2010, methodology): “More generally, the study we are presenting here is the most recent in a series of examinations carried out using linear and generalized linear models to relate covariates to the performance of face recognition algorithms [9, 10].”
50 years of biometric research: Accomplishments, challenges, and opportunities (2016, unknown): “However, this is challenging in practice because it is very difficult to isolate the effect of aging phenomenon from other types of noise affecting a biometric measurement [10] [43] [49] .”
Longitudinal Study of Automatic Face Recognition (2018, background): “[18] apply generalized linearmixedmodels to verification decisions (accept or reject) made by three algo-”