Accuracy Comparison Across Face Recognition Algorithms: Where Are We on Measuring Race Bias?

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 scenario modeling) and methodological considerations for assessing race bias in algorithms. We discuss data-driven facto...

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Veröffentlicht in:	IEEE transactions on biometrics, behavior, and identity science Jg. 3; H. 1; S. 101 - 111
Hauptverfasser:	Cavazos, Jacqueline G., Phillips, P. Jonathon, Castillo, Carlos D., O'Toole, Alice J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Artificial neural networks Bias Computational modeling Convolutional neural networks deep convolutional neural networks Demographics Face recognition Face recognition algorithm Faces Image quality Modelling Object recognition Population statistics Prediction algorithms Principal component analysis Race race bias the other-race effect deep convolutional neural networks the other-race effect face recognition algorithm race bias
ISSN:	2637-6407, 2637-6407
Online-Zugang:	Volltext
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