The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

Biometric systems encounter variability in data that influence capture, treatment, and u-sage
of a biometric sample. It is imperative to first analyze the data and incorporate this …

Racial bias is an important issue in biometric, but has not been thoroughly studied in deep
face recognition. In this paper, we first contribute a dedicated dataset called Racial Faces in …

Previous generations of face recognition algorithms differ in accuracy for images of different
races (race bias). Here, we present the possible underlying factors (data-driven and …

This paper describes the large-scale experimental results from the Face Recognition Vendor
Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at …

Although deep face recognition has achieved impressive progress in recent years,
controversy has arisen regarding discrimination based on skin tone, questioning their …

In this paper, we define meta-recognition, a performance prediction method for recognition
algorithms, and examine the theoretical basis for its postrecognition score analysis form …

Psychological research indicates that humans recognize faces of their own race more
accurately than faces of other races. This “other-race effect” occurs for algorithms tested in a …

Covariates are factors that have a debilitating influence on face verification performance. In
this paper, we comprehensively study two covariate related problems for unconstrained face …

The performance of face recognition algorithms is affected by external factors and internal
subject characteristics. Identifying these aspects and understanding their behaviors on …