Better face recognition exposes limits of image quality scores

Face recognition algorithms are becoming increasingly capable of extracting identity information from poor images.

Image quality and recognition utility are related, but they are not the same thing.

NIST tests whether poor quality predicts recognition failure NIST’s ongoing Face Analysis Technology Evaluation (FATE) Quality Summarization Track tests whether quality assessment algorithms, or QAAs, can predict false non-matches.

DHS finds matchers succeeding on low-quality images The Open Source Face Image Quality tool, or OFIQ, is one of the industry’s major attempts to standardize face quality assessment.

It is determining whether that image contains enough usable identity information for the recognition system that will actually process it.