Performance of Biometric Quality Measures

By Patrick Grother, Elham Tabassi. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

359 citations recorded by the source corpus.

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Predicting Failures of Vision Systems (2014, methodology): “Image quality assessment: The biometrics community has been using image quality measures as predictors for matching performance [20, 53].”
MagFace: A Universal Representation for Face Recognition and Quality Assessment (2021, methodology): “Following previous work [12, 36, 4], we evaluate the quality assessment on LFW/CFPFP/AgeDB via the error-versus-reject curves, where images with the lowest predicted qualities are unconsidered and error rates are calculated on the remaining images.”
FRVT 2006: Quo Vadis face quality (2010, background): “Researchers in fingerprint recognition have reported success in their efforts to develop universal measures of biometric quality [2, 23, 24].”
Demographic Estimation from Face Images: Human vs. Machine Performance (2015, background): “While face image quality is not easy to define, it is influenced by factors such as IPD, pose, blur, illumination, etc. [69], [70].”