  The recognition performance of biometric systems strongly depends on the
quality of the compared biometric samples. Motivated by the goal of
establishing a common understanding of face image quality and enabling system
interoperability, the committee draft of ISO/IEC 29794-5 introduces expression
neutrality as one of many component quality elements affecting recognition
performance. In this study, we train classifiers to assess facial expression
neutrality using seven datasets. We conduct extensive performance benchmarking
to evaluate their classification and face recognition utility prediction
abilities. Our experiments reveal significant differences in how each
classifier distinguishes "neutral" from "non-neutral" expressions. While Random
Forests and AdaBoost classifiers are most suitable for distinguishing neutral
from non-neutral facial expressions with high accuracy, they underperform
compared to Support Vector Machines in predicting face recognition utility.
