Gallery-Query Protocol for Evaluating Face Image Quality Metrics
Haoyu Chen, Praneet Singh, Edward J. Delp, Amy R. Reibman · 2023
As more automated face recognition systems are integrated into society, face image quality estimators (QE) become important. These QEs help quantify whether an input face image contains reliable information necessary for face recognition. However, to assess the effectiveness of face QEs, it is essential to have reliable evaluation protocols. Current face QE evaluation protocols require long computation times and do not have explicit real-world implications. In this paper, we propose a novel face QE evaluation protocol named “Gallery-Query (GQ) Protocol”. The GQ protocol is significantly faster in evaluating face QEs when compared to previous approaches. Furthermore, it has a very explicit real-world use case in constructing an optimal gallery set for face recognition tasks. In addition to this, we used these evaluation protocols to investigate the generalizability of face Quality Estimators (QEs) across various face recognition models, which also has implications for real-world use cases.